Secure Talk Using AI to Protect Global Communications & Privacy

20 Feb 2026 18:00h - 19:00h

Secure Talk Using AI to Protect Global Communications & Privacy

Session at a glance

Summary

This discussion centered on the growing threat of digital fraud and scams, and how AI-driven solutions can protect citizens and secure the digital economy. The event was hosted by Tanla Platforms to showcase their Wisely.ai platform, an agentic AI system designed to identify, prevent, and eliminate spam and scam communications.


The main fireside chat featured Vikram Sinha, CEO of Indosat Ooredoo Hutchison, who shared how his company transformed from viewing fraud as a customer complaint issue to treating it as a board-level strategic priority. He revealed that Indonesians lose $5 billion annually to scams, with 65% of citizens facing spam or scam attempts weekly. After implementing Tanla’s AI solution, Indosat saw significant improvements including 9% ARPU growth compared to the industry’s 3%, and customer churn rates dropping from 3.6% to 1.6%.


A panel discussion with leaders from banking, fintech, and payments sectors explored why fraud remains persistent despite technological advances. Panelists identified key challenges including the fragmented nature of defenses across interconnected systems, sophisticated AI-powered scamming techniques, and the need for better data sharing across institutions. They emphasized that scams involve complex behavioral journeys that begin long before actual transactions occur, making them difficult to detect at payment points alone.


The discussion highlighted that while individual institutions are implementing AI-driven fraud detection, the attack surface is interconnected but defenses remain siloed. Speakers called for coordinated intelligence sharing, national-level initiatives, and stronger law enforcement to combat what has become an industrial-scale, cross-border criminal enterprise that threatens the foundation of the digital economy.


Keypoints

Major Discussion Points:

AI-Powered Anti-Fraud Platform Launch: The introduction of Wisely.ai, Tanla’s agentic AI platform designed to identify, prevent, and eliminate spam and scam communications at scale, with live deployments at Indosat Indonesia and BSNL India protecting millions of users in real-time.


Scale and Impact of Digital Fraud: Discussion of the massive global economic impact, with over $1 trillion lost annually to scams worldwide, $5 billion lost by Indonesians in 2024 alone, and 65% of Indonesians facing spam/scam attempts weekly, highlighting the urgent need for systematic solutions.


Ecosystem-Wide Collaboration Requirements: Emphasis on how fraud prevention cannot be solved by individual institutions alone, requiring coordinated intelligence sharing between banks, fintech companies, telecom operators, payment platforms, and regulatory bodies to address the interconnected nature of digital fraud.


Business Results and ROI of AI Implementation: Concrete business outcomes shared by Indosat CEO, including 9% ARPU growth (vs 3% industry average), customer churn reduction from 3.6-3.7% to 1.6%, and protection of an estimated $500 million in potential losses within six months of deployment.


Future Vision for Intelligent Networks: BSNL’s roadmap for AI-integrated telecommunications infrastructure, including customer-controlled network resources, federated learning at edge data centers, and building networks where citizens can be “100% safe,” representing the evolution from connectivity providers to protection platforms.


Overall Purpose:

The discussion served as a product launch and industry forum for Tanla Platforms to showcase their AI-driven anti-fraud solution while bringing together telecom operators, financial institutions, and technology leaders to address the growing threat of digital fraud and scams across South and Southeast Asia.


Overall Tone:

The discussion maintained a professional and urgent tone throughout, beginning with celebratory product launch energy but quickly shifting to serious concern about the scale of fraud affecting vulnerable populations. The tone became increasingly collaborative and solution-focused as speakers emphasized the need for industry-wide cooperation, ending on an optimistic note about technology’s potential to create safer digital ecosystems for citizens.


Speakers

Speakers from the provided list:


Wish Gurmukh Dev – Host/MC representing Tanla Platforms and group companies (Carex and Value First)


Sanjay Kapoor – Host for fireside chat, distinguished global telecom leader, former CEO of Bharti Airtel, board member at Tanla Platforms with nearly four decades of telecom experience


Vikram Sinha – President, Director and CEO of Indosat Orido Hutchison, seasoned global telecom leader with experience across Asia and Africa markets


Anshuman Kar – Chief Customer Success Officer of Tanla Platforms, moderator for the panel discussion on AI for Citizen Protection


Ratan Kumar Kesh – Executive Director and Chief Operating Officer of Bandhan Bank, leading technology, operations, customer experience and transformation functions


Bipin Preet Singh – Founder and CEO of MobiQuik, leading fintech entrepreneur in India’s digital payments space


Neha Gutma Mahatme – Director at Amazon Pay India, payments and fintech leader driving customer-centric digital financial experiences


A. Robert J. Ravi – Chairman and Managing Director of BSNL, telecom leader with over three decades of service, gold medalist in electronics and communication engineering


Audience – Attendees asking questions during the panel discussion


Additional speakers:


None identified beyond the provided speakers names list.


Full session report

This comprehensive discussion examined the escalating threat of digital fraud and the emergence of AI-driven solutions to protect citizens and secure the digital economy. Hosted by Tanla Platforms to showcase their Wisely.ai platform to enterprise customers, telco partners, and board members, the event brought together telecommunications operators, financial institutions, and technology leaders to address what has become a crisis-level challenge across South and Southeast Asia.


Event Context and Tanla’s Vision

The event was structured around Tanla Platforms’ demonstration of their Wisely.ai platform, with Wish Gurmukh Dev presenting the company’s three foundational principles that shaped the AI solution: innovation, collaboration, and impact. As a three-decade-old company with group entities including Karix and Value First, Tanla positioned this platform as their response to industrial-scale digital fraud affecting billions of users across their markets.


The Scale and Urgency of Digital Fraud

Sanjay Kapoor, serving as moderator and Tanla board member, opened with sobering global statistics: consumers have lost over $1 trillion to scams, with digital fraud evolving from isolated phishing attempts to AI-powered, cross-border, automated operations at industrial scale. The regional impact proves equally devastating, with specific examples highlighting the crisis magnitude.


Vikram Sinha, CEO of Indosat Ooredoo Hutchison, shared his awakening to the problem during a MasterCard advisory board meeting in London in early 2024, where he learned that Indonesian citizens lost $5 billion in 2024, with 65% of the population facing spam or scam attempts weekly. This revelation transformed his company’s approach from treating fraud as a customer service issue to a board-level strategic priority.


The Indian context, as presented by Anshuman Kar, reveals equally concerning statistics. A Supreme Court judgment identified losses of 54-56,000 crores to scams, affecting demographics ranging from senior citizens to IIT professors. The channel analysis shows that 70% of scams in India originate from SMS, with 65 billion SMS messages and 15 billion over-the-top (OTT) messages sent monthly, creating an enormous attack surface.


These figures transformed the discussion from abstract business concerns to urgent social responsibility, particularly as the fraud disproportionately affects vulnerable populations including middle-income and lower-income women, elderly citizens, and rural communities who may lack digital literacy to recognise sophisticated scams.


AI-Driven Solutions and Measurable Impact

The centerpiece discussion featured Vikram Sinha’s fireside chat with Sanjay Kapoor, detailing Indosat’s implementation of Tanla’s Wisely.ai platform. The agentic AI system, designed to identify, prevent, eliminate, and prosecute spam and scam communications at scale, is currently live at Indosat in Indonesia and BSNL in India, protecting millions of users in real-time.


Vikram provided compelling evidence of the platform’s effectiveness through specific metrics: “Close to 2 billion spam instances, scam clearly threat intelligence protected. 2.3 million scammers flagged.” More significantly, the business impact proved substantial. Following AI implementation, Indosat achieved 9% ARPU (Average Revenue Per User) growth compared to the industry average of 3%, while customer churn rates dropped dramatically from 3.6-3.7% to 1.6%.


The transformation extended beyond metrics to customer perception. Vikram recounted visiting a village where a customer, when asked what they liked about Indosat, specifically mentioned spam and scam protection rather than traditional service features. This anecdote illustrated how fraud prevention had evolved from a cost center to a core business differentiator providing “peace of mind” alongside connectivity.


The Ecosystem Challenge and Fragmented Defenses

The panel discussion, moderated by Anshuman Kar, revealed the interconnected nature of digital fraud versus the fragmented approach to defense. Bipin Preet Singh from MobiKwik illustrated this challenge, noting that 99% of fraud complaints his company receives involve money stolen from other banks but routed through their platform, highlighting how companies become unwilling participants in fraud chains.


Ratan Kumar Kesh from Bandhan Bank, which processes 60 lakh (6 million) UPI transactions daily, revealed two primary fraud challenges: customers being defrauded and accounts being used as “mules.” The latter proves more troubling, as it involves willing participation from citizens who rent their accounts for easy money. He provided specific examples of fraudsters offering employees double their salary—from ₹70,000 to ₹1,50,000—to share banking data, creating persistent vulnerabilities that technology alone cannot address.


Neha Gutma Mahatme from Amazon Pay India provided crucial insight into why traditional fraud prevention fails: scams involve behavioral journeys that begin long before payment transactions occur. Social engineering, deepfakes, voice cloning, and fake identities create layered deception that makes detection at the transaction point insufficient. Furthermore, she highlighted the asymmetric nature of the AI arms race, where offensive AI operates unconstrained while defensive AI faces privacy regulations, customer experience requirements, and compliance constraints.


Strategic Partnerships and Implementation Philosophy

Vikram Sinha emphasized the importance of strategic partnerships over vendor relationships, noting that solving real problems at scale requires deep collaboration. Indosat’s approach included engineering collaboration with Tanla, utilizing their own GPU clusters and AI factory to train models specifically for their market, including language nuances and local fraud patterns.


This contrasted with approaches favoring company-specific models. Bipin Preet Singh argued that individual fraud models trained on proprietary datasets perform better than industry-level solutions, as they capture unique transaction patterns and customer behaviors. However, this approach faces limitations when fraudsters operate across multiple platforms and institutions, highlighting the ongoing tension between customization and ecosystem-wide protection.


Regulatory Response and National Initiatives

An audience question about government initiatives revealed evolving regulatory responses. The Reserve Bank of India has established a Digital Payments Intelligence Authority to enable national-level data sharing across the payments ecosystem. The RBI’s “mule hunter” initiative represents another coordinated effort to identify and prevent account misuse.


However, speakers noted that regulatory responses often involve adding friction to transactions to prevent easy fraud, potentially impacting the seamless user experience that has driven digital adoption. Law enforcement gaps emerged as a persistent challenge, with speakers noting that despite knowing fraud origins and having technology to track perpetrators, effective coordinated response remains insufficient to create deterrent effects.


BSNL’s Vision for AI-Integrated Infrastructure

A. Robert J. Ravi, Chairman and Managing Director of BSNL, outlined an ambitious vision for AI-integrated telecommunications infrastructure. The company is implementing AI across network operations to enable intelligent customer-network interactions, where users could request specific bandwidth or security levels in real-time. His brief presentation touched on federated learning concepts and edge computing approaches that could provide localized fraud protection while addressing privacy concerns.


The vision extends to rural protection through distributed AI capabilities, representing a significant evolution from traditional connectivity provision to comprehensive citizen protection platforms.


Customer Experience and Protection Balance

A recurring theme throughout the event was balancing security measures with customer experience. Traditional approaches often create friction that impacts legitimate users while failing to stop sophisticated fraudsters. The AI-driven approach demonstrated by Indosat shows that protection can enhance rather than hinder customer experience, as evidenced by improved customer retention and satisfaction metrics.


Customer education emerged as equally important as technological solutions. The sophistication of modern scams, including AI-generated voice cloning and personalized social engineering, means that even technically sophisticated individuals can fall victim, requiring comprehensive awareness programs alongside technological defenses.


Unresolved Challenges and Future Directions

Several critical challenges remain unaddressed. International coordination mechanisms for cross-border fraud prevention are underdeveloped, despite fraudsters operating globally. Data sharing limitations across institutions continue to hamper comprehensive fraud detection, even with national initiatives underway. The economic incentives driving mule account participation require solutions beyond technology, potentially including alternative economic opportunities and stronger legal deterrents.


The discussion also highlighted the need for standardization of fraud prevention protocols across different types of financial institutions, and the development of more effective methods to detect malicious intent rather than just transaction anomalies.


Implications for Digital Economy Development

The conversation revealed that digital trust has evolved from a desirable feature to critical infrastructure for economic development. With India’s digital economy projected to cross $1 trillion by 2030 and Indonesia’s already exceeding $100 billion in gross merchandise value, fraud prevention becomes essential for sustaining growth and inclusion.


The transformation of telecommunications companies from connectivity providers to protection platforms represents a fundamental shift in industry responsibility, making protection a core business differentiator rather than a cost center.


Conclusion and Path Forward

The event demonstrated that while individual institutions are implementing sophisticated AI-driven fraud detection, the interconnected nature of digital fraud requires coordinated ecosystem response. The success of platforms like Wisely.ai shows that real-time, AI-driven protection can deliver both citizen safety and business value when implemented through strategic partnerships and comprehensive approaches.


The path forward requires continued innovation in AI capabilities, enhanced data sharing mechanisms, stronger law enforcement coordination, and recognition that digital trust is fundamental infrastructure for economic development. As A. Robert J. Ravi emphasized, the ultimate goal is building systems where citizens can be “100% confident in network safety,” transforming digital protection from reactive fraud detection to proactive citizen security.


This transformation represents not just technological advancement but a fundamental reimagining of corporate responsibility in the digital age, where protecting citizens becomes as important as serving them, and where AI serves as the enabling technology for creating trustworthy digital ecosystems at national scale.


Session transcript

Wish Gurmukh Dev

Thank you everyone. Thank you very much. Thank you. Once again, ladies and gentlemen, a very good evening and welcome to what promises to be a truly memorable evening. On behalf of Tanla Platforms and our group companies, Carex and Value First, I extend a warm and a hearty welcome to our enterprise and telco customers, our global strategic partners, board members, and our incredible team. At the core of Tanla’s DNA are three enduring principles, innovation, collaboration, and impact. For three decades, this DNA has driven us to build innovation at scale, touching billions of users, and what excites us the most is the greenfield landscape that we have always explored. Along with it, it has helped us work in close partnership with our esteemed customers, our regulatory ecosystem, our telco partners, and the broader ecosystem to ensure that every step has been collaborative and always ahead of the curve.

And lastly, it has helped us ensure that every innovation we pioneer creates a tangible and a measurable impact in the world. And it’s these principles that shaped Wisely .ai, our agentic AI platform built to identify, prevent, eliminate, and bring to the books the growing menace of spam and scam, not just in India, but world over. Today, Wisely .ai is live and delivering real impact at Indosat in Indonesia, at BSNL in India, and with our leading banks in India, safeguarding millions of users in real time every single day. Tonight, we don’t just want to talk about it, we want to bring it to life. So without further ado, let’s bring the story to life. Please welcome our guest for the fireside chat.

A fireside chat from the theme Vision to Impact, driving customer engagement with AI -driven trust. It gives me immense pleasure to invite our host for the fireside chat, who has spent nearly four decades as a distinguished global telecom leader, leading one of India’s most iconic companies as CEO of Bharti Airtel, shaping global mobile policy as a key voice on the board and executive committee of GSMA, and building a legacy that stretches from telecom to digital services and beyond. We are honored to have him as our board member at Tanla Platforms, where his global perspective and vision, continue to shape our journey towards becoming a world -class AI -driven communications enterprise. Ladies and gentlemen, please put your hands together for Mr. Sanjay Kapoor.

A guest on the fireside chat is a seasoned global telecom leader who has not only defined the arc of the industry, but also built it. A career spanning across some of the most dynamic markets across Asia and Africa, he has held senior leadership roles from being CEO of Bharti Airtel Africa and Managing Director of Bharti Airtel Seychelles to serving as a CEO of Orido Group in Maldives and Director -CEO of Indosat Orido before taking on his current role. Today, he leads one of Indonesia’s most transformative telcos, Indosat Orido Hutchison, driving its evolution from a telco into an AI tech co, anchored by a bold vision. of AI for all and a deep commitment to digital inclusion and security for every Indonesian.

Please join me in welcoming the President, Director and CEO of Indosat Oridu Hutchison, Mr. Vikram Sinha. I hand over the baton to our esteemed host, Sanjay, to take it forward and we all look forward to it. Thank you.

Sanjay Kapoor

Thank you. Thank you for your kind words and welcome Vikram. Before we really get down to asking a few questions from the person who is going to be on the firing range for today’s chat, let me set up a pollute for what we are going to be discussing. We all know that the global economy is rapidly digitizing, but the trust has become its most crucial foundation. Digital payments are expected to surpass $14 trillion annually by 2027, with more than 5 billion people online. In South and Southeast Asia, nearly 2 billion people are coming online at a record speed, driven by affordable smartphones, low -cost data, and national digital infrastructure initiatives. India’s digital economy is projected to cross $1 trillion by 2030, while Indonesia’s has already exceeded $100 billion in GMV.

Yet, this scale brings vulnerabilities. Both markets are facing rising cybercrime, digital fraud, and organized scam operations, causing billions of dollars worth of losses each year. Globally consumers have lost over a trillion US dollars in scams Today’s fraud is no longer isolated phishing It is AI powered, it is cross border, it is automated and industrial in scale This is not just a consumer experience issue anymore It is an economic issue, it is a systemic risk issue, a trust issue and it demands great leadership to combat it It’s my privilege to welcome Vikram who I have known for years and years We worked together at ATL2 He is the President, Director, CEO of Indosat, Orido, Hutchison and is serving over 100 million customers in that country Under his leadership, Indosat has accelerated its transformation into a digital first AI technology access across both urban and rural communities.

Indosat has evolved as an AI tech company and partnered with Tanla, guided by a powerful vision, AI for all. And that’s a very powerful statement that they make. So Vikram, welcome here. And we’ll get down to some sharing of insights and questions to you. We’ve all known about digital fraud becoming more intense. We all know it’s eroding trust. And you as a CEO and with your lens, when did you really move it from being a customer complaint issue to a board level issue? Because there must

Vikram Sinha

First of all, again, it’s an absolute honor and privilege, especially having it with Sanjay. You know, I have a long learning history. So thank you. Thank you, Sanjay. And it’s an absolute honor. I think coming back to your question, let me share with all of you a true story. I’m also on the advisory board of MasterCard. I still remember early 2024, one of the board meeting in London, advisory board meeting, the Asia SCAM and the GASA, Global Anti -Scam Association, presented the data and I was blown off. That report shows that in 2024 itself, 5 billion US dollar Indonesians have lost. What touched me is these are all middle income, lower income women, elderly women.

This was an eye -opening data for me, number one. Number two, the next key highlight, Sanjay, it was every Indonesian, 65 % of the Indonesians are facing spam or scam on a weekly basis. So that itself was a trigger for me that Indosat being such an iconic brand. Let me tell you, Indosat is like BSNL of Indonesia. 58 year old company, first company to connect Indonesia to the world. It became Indosat Oridu Hachisan. But people have lot of expectation. So as a CEO, that was the trigger Sanjay that our role is not only to connect. Our role is to also protect my 100 million customer. And that is where I got very serious that we need to solve this problem for our 100 million customer.

Sanjay Kapoor

Yeah, I mean, I think every board worth its while today. gets intimidated by this problem that is hitting them. And I’m so glad to hear from you that your board is fully aligned with you on this cause and you’ve been able to convince them to say, I really want to go ahead making some serious investments and changes because of this. And my leading question from here is that I just said that scammers are using AI, voice cloning, automated phishing campaigns, synthetic identities. How did you think of AI in the middle of all this? You know, because it is a new technology. People are still surfacing where they’re headed. But you’ve picked it up as the foundational infrastructure for protecting, you know, this at a national scale.

So tell us about that.

Vikram Sinha

So let me put it this way. I’m a very strong believer of fake it till you make it. So I started talking about AI two years back and I had very little understanding. Of what AI will do. I’m telling you a true story. I was in. many people still struggle with that today. But let me tell you fast forward. I was invited by, I think, Sundar and Google Circle. There were 15 CEOs. This was around a year back. I was on a breakfast table. The joke started by saying that AI is everywhere other than P &L. This is how the breakfast started. But within an hour, I understood companies, countries who have been all in and ahead of the curve and who are solving real problem, they have started seeing value.

So for me, if I have to solve a real problem at scale, and that is where we said that if these scammers are using AI, we have heard many stories and the way they clone their voice, you know, you will be so scared what all are happening. Then we were very clear, we want a partner, we don’t need a vendor We want a partner who can work with us and use AI to solve this real problem And I have to say Sanjay, I think you are on their board, Uday is here We work with 96 vendors, we categorize among them 20 strategic partners But there are 4 or 5 where I invest time, which becomes very strategic for us Because I was trying to solve a real problem, I met Uday and then our commitment was aligned And that is how we wanted to make sure that not only we solve it, we do it in a way which should become a global case study

Sanjay Kapoor

And with an AI -led model that you have put in place What are the benefits that are accruing to you at a customer level to begin with?

Vikram Sinha

Yes, because as I said, you know, there’s a lot of AI as a toy. We are very clear what we don’t want to do. So now that this is my first showcase, I put it on my quarterly result. And then you have been a CEO, you have reported quarterly after quarterly, until unless you have a substance, you don’t put any example on your investor deck. So if you look at my last quarterly investor deck, we have put it there that with Tanla platform, three things I’ll highlight. You know, quarter four, ARPU grew for the industry 3%, we grew 9%. Number one. Number two, our churn. Our churn, because markets are mature. You know, you don’t have to be over -obsessed with gross assets.

And you know, you have to deliver experience. our churn for serious base greater than 90 days from a level of 3 .6, 3 .7 have come down to 1 .6. And this is just a beginning, Sanjay, you know, because the model is getting trained. And I’m very confident that we will see much more value going forward.

Sanjay Kapoor

And, you know, from here, being an ex -CEO and being a board member for very many years, now, this question of ROI always haunts every board to say, you’re making an investment in this, it seems to be doing good for your customers. What about the ROI on what you’ve done?

Vikram Sinha

You know, this is, again, and it’s a fair question, you know, investment on AI is not small. So until and unless you see the impact of AI on your P &L, it will not be scalable. So. So very clearly, within six, eight months, we have seen whether it is ARPU, whether it is churn. And the most important thing is, Sanjay, where we lost, if I go back to my last two decades of experience, we as Telco, we were very inward looking. The biggest thing which we missed was focusing on customer love. I think this is very fundamental. This problem which I am solving is so, so fundamental that the role of Telco is not only to connect, it is also to give peace of mind.

Protection is a big statement. And the channel which is getting used is voice, WhatsApp. So you need to solve it for your customer. Otherwise, you have no business.

Sanjay Kapoor

I mean, I hear you and you are a passionate CEO who believes in keeping his ears in and the ears out. And I see you

Vikram Sinha

I had no idea about Tanla or anything. In fact, first time when Uday came to meet me, I thought it was a startup. I’m again being very honest. But then somebody told me they are solving for banks in India. I think we have to understand if somebody is solving this problem for banks. Because, you know, spam is one thing. But the bigger issue is scam. And these scams which happen, these are small tickets. These are like 50 rupees, 100 rupees, 500 rupees. These are like 50 rupees. and it goes up to as high as $10 ,000. You know, I’m just giving you example. But then I realized that they have done some good work in India.

But I have to say, Sanjay, you know, where it moved from vendor to strategic partnership, we were also very keen that my team want to do a bit of an engineering with them. So we have a full stack AI factory. We have our own GPU cluster. I think there are a few things where we have done before India. Our cluster of GB200, H100 was live. So I told Uday, let’s train the data because see the power of compute and GPU. You know, we all talk about TikTok. TikTok was all designed on GPU. They don’t even use CPU. So if you have to be ahead of the curve, today on that platform, let me give you two data points.

Close to 2 billion spam instances. Scam. Clearly threat intelligence protected. 2 .3 million scammers flagged and customers are getting real time as you know we have grown up on the Airtel values I spent 5 days every month in the market I was in a village I was going to the new capital of Indonesia which is Far Flung on my way I stopped my car I saw an outlet I asked him my language still is not good in their language I asked him what do you like about Indosat IM3 he said this Sat Spam Spam Scam it’s solving real problem so again we just launched it on Whatsapp channel also I think Whatsapp is one of the challenge which is maximum getting misused so we have to continuously evolve and this is where Tanla has committed that we will make sure we do it together and we do it properly

Sanjay Kapoor

excellent you know these fireside chats have a time limitation so we have to keep it up and my stopwatch is telling me we’ve exceeded time already. So let me wind it off. Vikram, first and foremost, thank you for these insights. What stands out from our conversation today is how digital trust moves from concept to reality, which is what you’ve just described. When over 100 million subscribers are protected by AI, when billions of communications are analyzed in real time, and when millions of malicious actors are stopped within the ecosystem, trust is no longer a promise. It becomes an infrastructure. And what Indosat has shown through AI for All is that inclusion and protection are not trade -offs.

They must advance together. So thank you for your insights, Vikram, and it is a pleasure having you today.

Vikram Sinha

Thank you, Sanjay. Thank you.

Wish Gurmukh Dev

Can I request both of you just pose for a picture, please? Thank you very much, Sanjay. Thank you very much, Vikram. Wow. Two global leaders, one who defined the yesteryears of telcos across the world, and the other who’s redefining and bending the arc to set the future of telecom leveraging AI. Thank you so much, Vikram and Sanjay, for this scintillating talk. Thank you very much. Our next session, ladies and gentlemen, is… going to be a panel discussion wherein we have Anshuman Kaur Chief Growth my apologies Chief Customer Success Officer of Tanla Platforms who is going to moderate the panel AI for Citizen Protection and Securing the Digital Economy May I request Anshuman to kindly come on to the podium please First of all panelists Mr. Ratankesh Executive Director and Chief Operating Officer Bandhan Bank Bandhan Bank is one of the largest and the fastest growing banks in India with over 32 million customers served by the across 35 states He is leading multiple functions including technology, operations, customer experience and transformation functions second of our panelists Mr. Bipinpreet Singh founder and CEO MobiQuik a leading fintech entrepreneur at the forefront of India’s digital payments evolution Bipinpreet Singh has built MobiQuik into India’s largest digital wallet with over 180 million users please welcome Mr. Bipinpreet okay we’ll go ahead with the third panelist while we wait for Bipin our third panelist Ms. Nehaji Mahatme director Amazon Pay India a payments and fintech leader driving customer centric digital financial experiences at scale shaping how millions transact seamlessly and securely through the Amazon Pay India app please welcome Ms. Nehaji Mahatme Ms. Neha Kavya can I request you to just check with Bipin please Maybe Anshuman.

Yeah. Oh, Bipin is on his way. To Bipin, ladies and gentlemen, founder and CEO Moby Quick, leading fintech entrepreneur at the forefront of India’s digital payments evolution. Thank you.

Anshuman Kar

Good evening, everyone. As we just heard in this fireside chat, the problem is big. We just heard numbers of over… over $1 trillion being lost in the global economy because of scams and frauds. If you think about India in particular, SMS as a channel, almost 70 % of the scams originate from that channel. And as messaging itself has expanded into other OTT channels as well, and I’ll share some numbers, now 65 billion SMSes are spent monthly in India. Another 15 billion are sent monthly over OTT channels. So when you look at these numbers, it is clear that it is a channel, while it is important and critical, it’s only proliferating even further. So in that context, when you think about, and I joined Tanla relatively recently, compared to the three decades of history as a chief customer officer, and it has been a privilege to see the build and the deployment of WISD AI platform.

And it is an honor to have Vikram here. As a CEO, and there is nothing better to hear that validation directly from the customer. And you heard the impact that it’s having. on the end users in terms of protecting them from scams and spams. In fact, the estimations are within six months of launch, we have protected almost $500 million in estimated losses. And then as you think about where this takes us in the future, scams and scamsters are continuing to evolve. They’re not sitting idle. And so we have to stay a few steps ahead in the innovation curve. That becomes critical. And when they’re becoming more sophisticated, they’re becoming more personalized, and they’re actually probably also becoming more successful at times.

So tonight, I will not, before I get into solutions, I want to focus on the problem. Is the problem really getting better? Or is it getting worse? And why? So we have a distinguished panel here, and they all provide very different vantage points in the industry. We have banking, who sees the transaction risk and, frankly, a lot of regulatory accountability as well. You have fintech, who sees a lot of velocity and scale, and they also are obsessed with customer experience. And then you have platforms like Amazon Pay that have the commerce side, they have the payment side. So they see a lot of behavior signals across multiple parts of the platform. But from a citizen perspective, an average user, it’s a seamless journey.

They don’t operate in this individually. So that’s something that we will delve into. And as part of that, we would love to deep dive in terms of, how the key stakeholders in this ecosystem need to work to thwart this menace that is in front of us. So with that, I want to welcome our distinguished panelists. Thank you for joining us for this discussion. Thank you. So let me start off with you, Pratham. Recent Supreme Court judgment, a couple of weeks back, talked about, I think, 54 or 56 ,000 crores being lost to scams. In fact, they called it dacoity. I don’t think I’ve heard that term lately. I think it was around Chambal and all.

I used to watch movies when I had heard that term. But it is of that magnitude and scale. So the question, Pratham, is why is this still a problem? And what is really not working?

Ratan Kumar Kesh

mostly senior citizens and at times even IIT Bombay professors so that’s like the spectrum you can look at it they are being defrauded the second is a lot of customers are now able to open accounts in most of the banking companies they open banking accounts and those accounts are being utilized to siphon off the funds stolen from somewhere and getting routed so there are two parts of the problem in different sense the bigger trouble is the second one the second one is being done willingly with a country having 1 .5 billion population there are a lot of people who would be willing to open accounts and then across multiple banks the India stack makes it pretty simple to have an account onboarded very quickly in just about few minutes and then they go and rent that account and get a fee per month and that number and the lure of making easy money is so high and that’s why it’s so difficult and to me that’s not working So at one end, we celebrate India AI Summit, all the global leaders, heads of states, the big AI celebrities are coming all over here.

And we are talking about our countrymen who are actually defrauding poor senior citizens, and they have got hard -earned money of their entire life, and those are being siphoned off. So that’s very, very sad to see. So I think what is not working is the mindset. It’s not going to stop so soon. So it’s a bit of a philosophical response, but I’ll come to the bit more technical response a little later. The second is our customers getting defrauded using multiple things. That part I think it has got improved significantly because most of the banks now have very sophisticated rule engines. So now as banks like ours, we process. millions of transactions. Like I was just saying that my UPI volume, we are just an 11 -year -old bank.

My daily UPI volume is something like 60 lakhs per day. Now, the volume and velocity of transactions are very, very high. But the good part is that depending on the customer’s profile, the customer routine transaction pattern, we can identify an out -of -routine transaction. If a customer happens to withdraw 10 ,000 rupees from a particular ATM, generally, he or she withdraws from somewhere else, we can say it’s a non -routine transaction. Someone withdraws 10 ,000, suddenly withdraws 25 ,000, we know it’s a non -routine. Somebody never makes a rent payment, suddenly starts making a rent payment using one of the payment channels, we say it’s an out -of -routine. So once you have out -of -routine transactions, we are able to identify those.

Sometimes we prevent those transactions. Sometimes we go back, do enhanced due diligence, and then allow. So that part is working fine, and the tools are getting more and more mature. AI is helping us to build the algo a lot better. So that part is clearly working. So if you are seeing the numbers, the fact that the velocity and the volume has gone up in terms of percentage is coming down. But the mule part of it is very, very scary, which is customer account being utilized as a rent. You know, the other day, one of my employees from the fraud prevention team called up one of the fraudsters, saying that, you know, this is a senior citizen, you called so many times, you tried to defraud, why are you doing this?

So his response was that, can you tell me how much money you make a month? It must be 50 ,000, 70 ,000? I’ll give you double, you start giving me data. The fraudster is telling my employees, saying that, can you share with me more data? Don’t worry, I’ll not tell anybody, just give me data, I’ll give you 70 ,000, I can even pay you 1 ,50 ,000 per account. Now, there again, we are using a whole bunch of technology, including the algorithm in terms of the transaction monitoring algorithm to really prevent that. And I think Carix has developed one which we are implementing now, which is this anti -phishing tool. which has got some very interesting capabilities.

If some of you are interested, you could talk to the Karik spokes. I think that is quite interesting. They sit on the DLT platform. They scan that particular SMS, where is it originated from, some of the links that they provide in terms of collecting data, whether it is genuine or fake. They look at the keywords using some of the AI algo and then come back and use some of the techniques to stop preventing and sending the SMS. To the potential customer who could then get defrauded. So I think these are some of the things which are working. So largely that is what is the spectrum I would see, Ansuman. It is a long answer, but that is what it is.

Anshuman Kar

No, this is fantastic insight and it is obviously great. In fact, my parents are so scared they do not even use ATM cards because of the risk of being defrauded. In fact, they wait for me to come and they will do recharge on the phone, otherwise they are going to the stores to do it. Because this is becoming really scary and especially vulnerable populations, like senior citizens and all, are particularly exposed. let me ask let me turn it over to you Neha right you sit at the intersection you see AI patterns I’m sure Amazon uses AI all over you analyze behavior patterns and all across commerce across payments right but why are we not able to stop scams across the whole journey

Neha Gutma Mahatme

so I want to kind of talk about three four aspects that we’ve learned on why it is difficult so first of all I think GAMP is not at a transaction or a payment transaction level I think it is a behavioral journey it starts much before the payment really happens and that’s really where the fundamental issue is and that’s what I think as an industry we need to kind of solve for the second and if I kind of belabor on that point I think the social engineering happens much ahead it is not really when the transaction is happening or the payment is happening the deepfakes, the voiceovers the fake identities the layering of transactions, I think it’s all making it very difficult to stop scams at the point of the transaction.

The second point is the data side was limits visibility. So while as Amazon we have really good data internally on the platform but I think we miss the data of how these social engineering patterns are getting created outside of Amazon and that really prevents us. The third is the human psychology evolves faster than models. So while you can really build models, refine algorithms but can’t beat the offensive AI because the AI is being used on both sides. It’s not that the preventive AI is working, there is also offensive AI. And the offensive AI works unconstrained while the defensive AI has constraints of privacy, constraints of regulations, constraints of customer experience. And so I think that’s the reason why I think that’s the reason why I think that’s the reason why I think that’s the reason why I think that’s the reason why I think that’s the reason why I think that’s the reason why I think that’s the reason why I think that’s the reason why I think that’s the reason why I think that’s the reason why I think that’s the reason why I think that’s the reason why I think that’s the reason why I think that’s the reason why I think that’s the reason why I think that’s the reason why I think that’s the reason why I think that’s the reason why I think that’s the reason why I think that’s the reason why I think that’s the reason why I think that’s the reason why I think that’s the reason why I think that’s the reason why I think that’s the reason why I think that’s the reason why I think that’s the reason why I think that’s the reason why I think that’s the reason why I think that’s the reason why I think that’s benefits that you need to provide.

So I think that’s the third part and therefore the last part which is really the crux of the point is that AI is helping detect anomalies. It is not helping detect the malintent or the behavior and unless and until I think we solve the malintent or the behavior I think the scans, the

Anshuman Kar

It’s a fantastic response. And basically what you’re saying is no one institution is seeing the whole journey. You’re seeing pieces of it. But there’s a lot of parts that are interconnected. Let me bring Vipin into the conversation. Vipin, you see a lot of transaction velocity and scale. But you’re also focused on customer experience because that can become a friction point if you do go overboard in times to protect. How does AI come in? How do you calibrate your model and AI to balance that potential friction that potentially can impact your growth and legitimate customers versus protecting from fraud?

Bipin Preet Singh

Thank you. Thank you, first of all, Ashutosh and Tanla Solutions for inviting me here. It’s a privilege. We are also customers of Tanla, so happy customers. Thank you. I think when it comes to AI and the usage of AI for fraud, I want to give some perspective with respect to first the kind of fraud. So we operate in payments and financial systems. And I think what’s happened in the last 10 years is the financialization and digitalization of financials has happened at an exponential scale. And so many different entities have gotten interconnected, just like what you are saying, that a loophole in just one place is sufficient to create fraud and scam throughout the ecosystem. So one thing we have to be very clear that one entity cannot control scams.

It’s very, very difficult. Because like the experience that we have at MobiQuake is 99 % of whatever scams that our customers complain of are not the money. It’s not the money that they get stolen out of MobiQuake, but it is actually stolen out of some other bank. and come into MobiQuick. So we are the recipients and, you know, and we get the complaints that the money has come here and we need to take action, whether it is coming through UPI, coming through credit card fraud and all those things. Now, therefore, you know, the standards of education and the standards of 2FA, second factor authentication, they need to be there. Perhaps are not equally enforced. The awareness and the education is not equally enforced.

And that brings me to the second point, which is that, you know, the AI, the scams have also become very, very sophisticated. Right? In our company, and we are a fintech company, we employ so many smart people. There are people who have fallen fraud to scams where they got a WhatsApp from me asking to buy gift cards with my photo and people have bought it, you know, without trying to verify or seeing what the number is. On the WhatsApp thing, because, you know, this is… It’s becoming, with AI, it’s becoming harder and more sophisticated. You know, the modus operandi of the scamsters is becoming extremely smart. And they are getting very, very smart at understanding the profile of the customer.

It’s not, they don’t target everyone. They have a very clear idea on who is likely to fall for a scam. So, there is need, for example, and there is a, I think there is an effort going on at the RBI’s end. And the government said to create a intelligence body which will share data across the entire payments ecosystem. I think it’s called Digital Payments Intelligence Authority or something like that. And that is something that’s extremely important because until data sharing starts to happen at an India level scale, you cannot identify. I can identify patterns. I can identify a pattern which works for me. But, you know, the scamsters will get smarter because they will keep changing their MO outside of Mobiquit.

So it’s very very difficult to keep adapting to that. So there is a national level initiative that is required. Third thing which I want to say is on the LEA front. I think the almost the entire country all the police everyone knows where the scams are. And this I am not able to understand why no action gets taken. It is the same places. It is the same origins. Right. But somehow no action gets taken. And I think until there is fear of law until enforcement has happened that that which will fraud you know payments fraud will come. At our end you know what we are doing is we are creating our own in -house models.

So as far as technology is gone in terms of machine learning and now with AI. We have created our own models. We have created our own models trained on our own data sets because they work best. for our kind of transaction pattern. But they may not work best for the kind of solutions that other companies may need. In fact, we have explored solutions of fraud and machine learning from other companies, but they have a very poor performance because they are trained on some industry -level data, which is not the same patterns that we get. So, at our end, we are a tech company, we can adapt. But I cannot say the same for all the entities, at least on the financial domain.

And that’s a big problem because until there is a national… And I think the regulator, especially RBI, is very, very concerned about this. I think we have heard now, and in my recent conclave where I went to, that they are saying that enough of making transactions easy. Now we need to go in the reverse direction. Now we need to make transactions a little difficult so that there is some friction because otherwise, you know, people are losing money.

Anshuman Kar

It’s great points. And as you said, I think the silos of data that we have, and in fact, you talked about training the models. In fact, you know, again, when we went to Indosat as well, you had to go and train them, even including language nuances as well. So these things become critical in terms of adapting. You mentioned about like RBI initiative and all, and I direct this to you, Ruthen. Who ultimately owns the responsibility of protecting citizens at national scale? Because can banks do it alone? Can RBI do it alone? Or are you dependent on upstream and downstream signals, upstream signals like that you get from telcos because a lot of these things originate from the channels?

How should responsibility be structured? to protect people and to help.

Ratan Kumar Kesh

I think Vipin spoke about that point that look, for a fraud to happen, we know that somebody would have gone to an e -commerce platform to make a payment and that payment is coming through a payment channel and the account is held in Bandhan Bank and the payment is made to let’s say for a product or through some platform the payment is made to an access bank credit card. Now and then the fraudster is actually sitting somewhere out there who is pretty much somebody amongst us. Now I will give you a very funny story of I lived in Mumbai for 20 plus years the local trains are full of used to be those days pickpockets.

I came from I was living out of India and came back and I was going to meet a friend of mine that was my first trip in the local train and my purse got stolen and he said don’t worry how much money I said that’s okay but I had credit cards and all of that so somehow managed to block those cards and he says let’s go to the police station I said but I had my identity cards there he says don’t worry it’s fairly ethical pickpockets in Mumbai you go and tell the police you have to tell which train which local train from what to what and what exactly it must have opened probably so I said from Borivali to Andhri it must have happened in between it was a whatever 950 local train so after two days the police called me and handed over me the identity cards so so there was nothing else there but then I got back the identity card so I think that’s like the police has an ability to really find out who the people are and I find it quite baffling to figure out that this fraudster is somewhere there the telephone numbers are issued multiple of them by a telecom authority the other pan cards were issued which are used to get this sort of account opening and video QIC and the telephone numbers yet we are not able to find out who these people are and technology has to protect all of that which as all of us are saying that we are trying our best to protect and the bad part and the sad part is that whenever there is a fraud happens and a customer goes to the regulator like the ombudsman and it says 20 lakh fraud it’s okay which bank is went from it says it went from HDFC bank where did it fall first it fell in Bandhan bank okay two of you together 10 10 lakhs pay it and be done with this that’s easy isn’t it I mean we of course are regulated entity regulator has no choice but to sort of do whatever best they can do and which we do and that’s absolutely fine we must have had some lacunae in our process but how do the whole chain has to work together I mean including the citizens instead of becoming gullible in terms of having the awareness about banking product the banks and the payment companies and the country has to create more awareness the police, cyber police and the local police they have to work together minister of home affairs is working very hard in terms of really making it happen so it’s an ecosystem problem and I think if all of us come together, all of us create more awareness and make it really ruthless probably that’s the only way to happen otherwise it’s no easy

Anshuman Kar

Thank you, thanks a lot I am told time is up I wanted to solicit two questions as well from the audience but in the interest of time I will just summarize this session in fact we have all talked about going kind of end to end it’s not just identification using the AI models but the prevention, the elimination and ultimately holding the scampers accountable with law enforcement and that comes a big part there is law and the enforcement of the law they can sometimes mean two different things so ultimately one of the things I hope just one more point in the name of hyper personalization the amount of data that you collect and the amount of data that you collect and the amount of data that you collect and the amount of data that you collect and we have an ability to go back to Neha and say, you know what, you’ve been searching for a home.

I can tell you which is the right home for you. That’s great. Neha feels delighted because she can actually choose the right house. But the same data is getting misused to do other things. And as much as I can collect data as a bank or a real estate company, Proster can also collect the same data. So as you said, the AI is on both sides and they are like more offensive. And so it’s a question of who stays a few steps ahead of the other, right? What is striking from this discussion, hopefully, and I’m summarizing is like, as you can hear, everyone is doing something, right? Oh, please. Please go ahead. We can take one or two questions quickly.

Sure, please. Can you please help the gentleman with the mic? This is not the best way.

Audience

So there should be some integrated approach. So I think the government of India is already working on that and they have a digital payment intelligence platform. So it’s not for that purpose or you are referring to some other issue.

Anshuman Kar

Sorry, is that a question? I think the question is there are some government initiatives.

Audience

Initiative is already there. Is it not enough? To have an integrated model for this fraud protection and other thing. Because you said. financial institutions are having their own trend model and accordingly they are protecting their customer this thing but if we have that integrated model and government of India through their one company there they have initiated with the collaboration because that RBI is working on mule hunter through RBI mule hunter is the initiative yeah from RBI in his hub so that is already all the banks are doing and they are doing with their own three month five month data individually not a complete umbrella like that so for that everything is coming in the this digital payment intelligence platform so with this initiative Anna that it is so which you have referred will not be addressed to

Bipin Preet Singh

yes yes absolutely I think you know at least I feel that there is going to be there’s a strong potential because for the first time data across the financial economy is being used for the first time and it is being used for the first time ecosystem will come together in one place and I think that is a big deal and then once that data comes together then hopefully the best people will work on it and understand patterns at a national scale because the problem in digital is everything is connected so you have to study it in an integrated manner and I am very

Anshuman Kar

Thank you for that question and we are obviously hoping that all of these show results but at the same time we are talking about national but scammers are not limited to even national geographies they are international as well so the scope and the breadth the surface area of the threats are only expanding so we have to really look beyond and if I may from my personal experience one of the things is in the world of AI data is actually the differentiator not so much the models because all public and the willingness to share the data itself is a potential barrier real time data and this is where it is not about just banks and financial institutions it’s also potentially telecom, right, because they see a lot of initial signals in terms of messages being sent and communication and so forth, as Vikram just talked about as well.

So let me summarize again this session.

Audience

Can I ask one question?

Anshuman Kar

May I request you to take it offline, please, because of the time constraint, if you don’t mind. Thank you. Thank you for cooperating, sir. It’s great to hear. I’m sure there’s a lot of interest in this topic and it shows the resonance of what we are discussing. So, again, I think to summarize, as you said, the attack surface is all interconnected, but our defense is right now fragmented. And therein lies the opportunity. And while the next frontier cannot be just more smarter individual AI model, it has to be really coordinated intelligence. And that obviously has to happen real time. It has to happen across the ecosystem. And it has to happen within the guidelines of a national level trust architecture.

So with that I will want to thank you to all the panelists and also for all of you to participate in this discussion and really contributing to this to shaping what the future looks like because this is really not just trust, this is the foundation of creating the digital economy and the growth that underpins it. So thank you so much. Thank you very much all the panelists and Anshuman may I all request you to stay on the stage for a quick photograph. Wow. From using technology for transaction monitoring and layering it with AI to solving for behavioral intent and offensive AI along with solving for customer friction on customer experience by layering it on technology and captive models along with regulatory tenets.

It was a very insightful and a very meaningful panel. Thank you very much each one of you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Our last session for the evening is one of the most interesting ones. It’s what we do on our third element or the third pillar of DNA, that is impact. It’s the impact spotlight, wisely .ai, our client’s perspective. Very few leaders in India telecom landscape carry the depth of experience and the institutional weight that our next speaker brings to the stage. As chairman and managing director of BSNL, he has orchestrated one of the sector’s most compelling turnarounds, driving the rollout of India’s first indigenous 4G network and restoring the organization to a clear path of growth, profitability and purpose.

With over three decades of service spanning tri the government of Tamil Nadu and an advisory role to the government of Uganda, recognized with the Visist Sanchar. For distinguished service and a gold medalist in electronics and communication engineering, he remains one of the most. consequential voices in India’s telecom and digital governance story. Ladies and gentlemen, please put your hands together for A. Robert J. Ravi, Chairman and Managing Director, BSNL.

A. Robert J. Ravi

important step we are thinking about, that’s what I was talking about. On the network side, can I bring AI? In bringing AI in the network side, I can even get patents, customer patents, calling patents, network initiatives. We were able to even see exactly how and where most of the complaints were happening in the network. And this also helped me for tweaking my entire setup today, where I’m very sure at the end of probably the study and whatever research we are right now going on the AI, as a user, if you are a BSL customer, you can intelligently speak to your RAN. When I speak, when I tell intelligently speaking, it could be various things. So you can have… you can request a specific dedicated data specific dedicated voice traffic that means today I am in this place I need to video stream I need a 1G so not a 1G at least under 10 throughput available all the time it will be made possible so that type of a user enabled platform which we are building that will control not only from the customer angle from the network angle if this becomes successful today no customer in future when this reality actually comes in no customer could be so easily fished or smacked that’s the way which we are trying to go ahead of course going into what exactly happened was the last one we can see that the user impact we were able to authoritatively say that that so many number of connections to close to 280 million spans we have installed today.

This is on one side. Now we are integrating this particular aspect on a customer experience platform also. How do we benefit out of it? In my customer experience today also, we have brought in we have something called the AI Vani system. It’s just a Vani which comes and says, whatever you want, you can speak to the particular agent. And then we brought in something called a BSNR recharge expert system. It’s a complete AI driven. So when user suppose even as a user because spam suppose when he stopped the spam for the SMS side, next thing we have to concentrate is on the data side, which again I’m talking of course we are speaking to you all how do we do for the data side.

Data is not only on whatsapps or social media how can we expand the horizon of this particular area that’s where what we thought can I build in intelligence into the system itself so when you wanted to do probably recharge or even it works as a worm in the network to easily identify the sites which needs to be blocked which should not be available for my customers such a sort of independent intelligent network needs to be built in which we are targeting up that’s the second pillar the last pillar before I wind up is going to be the rural thing the rural side with Bharatanat coming in at a very big space rolling out of Bharatanat’s network we are trying to see when you’re closing closely going close to the customer at the end we are seeing lot of fractured coming in can I put edge data centers using this edge data centers can I really run what we call even SLMs.

Today we talk about different LLM models. These LLM models require a lot of information. The data is a key engine for it. And we are all travesty to see why should I share my data. So this is the next concept of what we bring in what we call a federated learning. So your data resides with you. I just learn your data and I federate over it. So all this is possible when I go to the rural end of the edges. So there I will be able to protect the customer to a next level. I am very sure I think we can keep talking on this very interesting topic but since the time is short I thank the organizers for giving me opportunity.

But my request to you all still there is lot of work to be done. Unless we have built a system where we confidently say our citizens that you are 100 % safe in my network. our job is not done. And this is possible only when we bring in technology and play it across in a platform which really intelligently builds this network. Thank you.

Wish Gurmukh Dev

Thank you, it’s been a wonderful evening absolutely thrilling to have two CEOs exchange and share with the audience the real life problems and how they converted into an opportunity that is going to shape the future of telecom in one part of the world followed by a panel thank you Anshuman and thank you once again to all the panelists who made the effort to come in and share their own perspectives on what could be changed structurally from a regulatory perspective, from an ecosystem collaboration perspective to customer experience without friction and lastly dear CMD Mr. Robert Ravi for sharing the deep collaboration that BSNL and Tandla platforms have come into and are trying to set a lighthouse to what could really be a customer experience driving safe and secure customer transaction thank you very much it’s been a true honor and a privilege to host everyone here thank you I am very thankful on behalf of Tanla platforms, our group companies Carex and Value First for hosting you all here Thank you very much Thank you Thank you.

S

Sanjay Kapoor

Speech speed

137 words per minute

Speech length

757 words

Speech time

329 seconds

Digital fraud as systemic risk

Explanation

Sanjay frames digital fraud as a systemic, economic threat that goes beyond individual consumer issues, emphasizing the need for strong leadership to address billions of dollars in losses worldwide.


Evidence

“Both markets are facing rising cybercrime, digital fraud, and organized scam operations, causing billions of dollars worth of losses each year.” [2]. “This is not just a consumer experience issue anymore It is an economic issue, it is a systemic risk issue, a trust issue and it demands great leadership to combat it” [3]. “We’ve all known about digital fraud becoming more intense.” [1].


Major discussion point

Scale and systemic risk of digital fraud


Topics

Building confidence and security in the use of ICTs | The digital economy


AI essential to combat AI‑powered scams

Explanation

Sanjay highlights that scammers are now leveraging AI technologies such as voice cloning and synthetic identities, making AI an essential defensive infrastructure.


Evidence

“And my leading question from here is that I just said that scammers are using AI, voice cloning, automated phishing campaigns, synthetic identities.” [51].


Major discussion point

AI‑driven solutions for fraud detection and prevention


Topics

Artificial intelligence | Building confidence and security in the use of ICTs


Board pressure for ROI on AI anti‑fraud

Explanation

Sanjay notes that boards constantly demand clear ROI evidence for AI investments, linking financial performance to the scalability of anti‑fraud initiatives.


Evidence

“What about the ROI on what you’ve done?” [109]. “And, you know, from here, being an ex‑CEO and being a board member for very many years, now, this question of ROI always haunts every board to say, you’re making an investment in this, it seems to be doing good for your customers.” [110]. “And I’m so glad to hear from you that your board is fully aligned with you on this cause and you’ve been able to convince them to say, I really want to go ahead making some serious investments and changes because of this.” [112].


Major discussion point

Business impact and ROI of AI anti‑fraud initiatives


Topics

The digital economy | Financial mechanisms


V

Vikram Sinha

Speech speed

145 words per minute

Speech length

1305 words

Speech time

537 seconds

Indonesia scam losses 2024

Explanation

Vikram reports that Indonesian consumers lost $5 billion in 2024, with a majority of victims being low‑income women and the elderly, underscoring the social dimension of fraud.


Evidence

“That report shows that in 2024 itself, 5 billion US dollar Indonesians have lost.” [16]. “Number two, the next key highlight, Sanjay, it was every Indonesian, 65 % of the Indonesians are facing spam or scam on a weekly basis.” [18]. “What touched me is these are all middle income, lower income women, elderly women.” [19].


Major discussion point

Scale and systemic risk of digital fraud


Topics

The digital economy | Closing all digital divides


Wisely.ai impact and ROI

Explanation

Vikram describes how the Wisely.ai platform delivers real‑time protection, driving a 9 % ARPU increase and cutting churn from 3.7 % to 1.6 % within a quarter.


Evidence

“And it’s these principles that shaped Wisely .ai, our agentic AI platform built to identify, prevent, eliminate, and bring to the books the growing menace of spam and scam, not just in India, but world over.” [48]. “Today, Wisely .ai is live and delivering real impact at Indosat in Indonesia, at BSNL in India, and with our leading banks in India, safeguarding millions of users in real time every single day.” [62]. “you know, quarter four, ARPU grew for the industry 3%, we grew 9%.” [103]. “our churn for serious base greater than 90 days from a level of 3 .6, 3 .7 have come down to 1 .6.” [104].


Major discussion point

Business impact and ROI of AI anti‑fraud initiatives


Topics

Artificial intelligence | The digital economy


AI for all – inclusive protection

Explanation

Vikram emphasizes Indosat’s “AI for all” vision, aiming to protect 100 million customers across urban and rural Indonesia, linking AI deployment with digital inclusion.


Evidence

“of AI for all and a deep commitment to digital inclusion and security for every Indonesian.” [14]. “Indosat has evolved as an AI tech company and partnered with Tanla, guided by a powerful vision, AI for all.” [64].


Major discussion point

Future vision – AI for all, inclusive protection, and federated learning


Topics

Artificial intelligence | Closing all digital divides


R

Ratan Kumar Kesh

Speech speed

181 words per minute

Speech length

1453 words

Speech time

480 seconds

Account‑rental (mule) fraud in India

Explanation

Ratan explains how rapid onboarding enables large‑scale account‑rental schemes, where stolen funds are funneled through rented bank accounts, creating a major fraud vector.


Evidence

“they open banking accounts and those accounts are being utilized to siphon off the funds stolen… rent that account” [27]. “But the mule part of it is very, very scary, which is customer account being utilized as a rent.” [28].


Major discussion point

Scale and systemic risk of digital fraud


Topics

The digital economy | Building confidence and security in the use of ICTs


Detecting out‑of‑routine transactions

Explanation

Ratan notes that banks can flag anomalous, out‑of‑routine transactions by comparing them to a customer’s normal pattern, helping to surface mule activity.


Evidence

“So once you have out‑of‑routine transactions, we are able to identify those.” [30]. “But the good part is that depending on the customer’s profile, the customer routine transaction pattern, we can identify an out‑of‑routine transaction.” [31].


Major discussion point

AI‑driven solutions for fraud detection and prevention


Topics

Artificial intelligence | Building confidence and security in the use of ICTs


A

Anshuman Kar

Speech speed

152 words per minute

Speech length

1866 words

Speech time

733 seconds

Fragmented defenses need coordinated intelligence

Explanation

Anshuman stresses that while the attack surface is highly interconnected, current defenses are fragmented and must evolve into a coordinated, real‑time intelligence layer.


Evidence

“So, again, I think to summarize, as you said, the attack surface is all interconnected, but our defense is right now fragmented.” [97]. “And while the next frontier cannot be just more smarter individual AI model, it has to be really coordinated intelligence.” [75].


Major discussion point

Ecosystem collaboration, data sharing, and regulatory role


Topics

Data governance | Building confidence and security in the use of ICTs


Need for ecosystem‑wide data sharing

Explanation

Anshuman highlights that data, not just models, is the key differentiator and calls for real‑time data sharing across banks, telcos, and regulators to close fraud loops.


Evidence

“if I may from my personal experience one of the things is in the world of AI data is actually the differentiator not so much the models because all public and the willingness to share the data itself is a potential barrier real time data and this is where it is not about just banks and financial institutions it’s also potentially telecom, right, because they see a lot of initial signals in terms of messages being sent and communication and so forth, as Vikram just talked about as well.” [15]. “It has to happen across the ecosystem.” [98].


Major discussion point

Ecosystem collaboration, data sharing, and regulatory role


Topics

Data governance | The enabling environment for digital development


N

Neha Gutma Mahatme

Speech speed

253 words per minute

Speech length

502 words

Speech time

118 seconds

Offensive AI outpaces defensive AI

Explanation

Neha points out that while defensive AI is constrained by privacy, regulation, and user experience, offensive AI operates without such limits, making it harder to stay ahead of fraudsters.


Evidence

“It’s not that the preventive AI is working, there is also offensive AI.” [57]. “And the offensive AI works unconstrained while the defensive AI has constraints of privacy, constraints of regulations, constraints of customer experience.” [58]. “So while you can really build models, refine algorithms but can’t beat the offensive AI because the AI is being used on both sides.” [81].


Major discussion point

AI‑driven solutions for fraud detection and prevention


Topics

Artificial intelligence | Building confidence and security in the use of ICTs


AI cannot infer malicious intent

Explanation

Neha argues that AI models struggle to detect the underlying malicious intent or human psychology, which evolves faster than any model.


Evidence

“The third is the human psychology evolves faster than models.” [85]. “It is not helping detect the malintent or the behavior and unless and until I think we solve the malintent or the behavior I think the scans, the” [86].


Major discussion point

AI‑driven solutions for fraud detection and prevention


Topics

Artificial intelligence | Human rights and the ethical dimensions of the information society


B

Bipin Preet Singh

Speech speed

158 words per minute

Speech length

949 words

Speech time

358 seconds

National data‑sharing authority (Digital Payments Intelligence Authority)

Explanation

Bipin describes the government’s plan to create a Digital Payments Intelligence Authority that will pool data across the payments ecosystem, enabling national‑scale fraud detection.


Evidence

“And the government said to create a intelligence body which will share data across the entire payments ecosystem.” [41]. “I think it’s called Digital Payments Intelligence Authority or something like that.” [42].


Major discussion point

Ecosystem collaboration, data sharing, and regulatory role


Topics

Data governance | The enabling environment for digital development


Fintechs build in‑house AI models

Explanation

Bipin explains that fintechs develop proprietary AI models trained on their own transaction data because external models perform poorly on their unique patterns.


Evidence

“We have created our own models trained on our own data sets because they work best.” [88]. “In fact, we have explored solutions of fraud and machine learning from other companies, but they have a very poor performance because they are trained on some industry‑level data, which is not the same patterns that we get.” [89].


Major discussion point

AI‑driven solutions for fraud detection and prevention


Topics

Artificial intelligence | Data governance


W

Wish Gurmukh Dev

Speech speed

82 words per minute

Speech length

1069 words

Speech time

773 seconds

Opening remarks on massive spam/scam impact

Explanation

Wish opens the session by stressing the global scale of spam and scam, positioning it as a critical threat that requires collaborative AI‑driven action.


Evidence

“And it’s these principles that shaped Wisely .ai, our agentic AI platform built to identify, prevent, eliminate, and bring to the books the growing menace of spam and scam, not just in India, but world over.” [48].


Major discussion point

Scale and systemic risk of digital fraud


Topics

Building confidence and security in the use of ICTs | The digital economy


DNA of collaboration and measurable impact

Explanation

Wish highlights Tanla’s core principles—innovation, collaboration, impact—and cites measurable outcomes such as ARPU growth and churn reduction as proof of ROI.


Evidence

“At the core of Tanla’s DNA are three enduring principles, innovation, collaboration, and impact.” [68]. “Today, Wisely .ai is live and delivering real impact at Indosat in Indonesia, at BSNL in India, and with our leading banks in India, safeguarding millions of users in real time every single day.” [62]. “And lastly, it has helped us ensure that every innovation we pioneer creates a tangible and a measurable impact in the world.” [116].


Major discussion point

Business impact and ROI of AI anti‑fraud initiatives


Topics

Financial mechanisms | The enabling environment for digital development


A

Audience

Speech speed

159 words per minute

Speech length

188 words

Speech time

70 seconds

Question on integrated fraud‑protection model

Explanation

The audience asks whether an integrated, real‑time model for fraud protection—beyond individual institution models—can be achieved.


Evidence

“To have an integrated model for this fraud protection and other thing.” [7]. “So I think the government of India is already working on that and they have a digital payment intelligence platform.” [39]. “So there should be some integrated approach.” [44].


Major discussion point

Ecosystem collaboration, data sharing, and regulatory role


Topics

Data governance | The enabling environment for digital development


Adequacy of existing government platforms

Explanation

The audience probes whether current initiatives like the Digital Payments Intelligence platform are sufficient for a unified, umbrella‑level fraud protection system.


Evidence

“financial institutions are having their own trend model … but if we have that integrated model and government of India … they have initiated with the collaboration because RBI is working on mule hunter … not a complete umbrella like that so for that everything is coming in the this digital payment intelligence platform” [29].


Major discussion point

Ecosystem collaboration, data sharing, and regulatory role


Topics

Data governance | The enabling environment for digital development


A

A. Robert J. Ravi

Speech speed

129 words per minute

Speech length

757 words

Speech time

349 seconds

AI deployment on network edge

Explanation

Ravi discusses bringing AI to the network layer, including edge‑based solutions and federated learning, to protect rural users and optimize telecom services.


Evidence

“On the network side, can I bring AI?” [60]. “It’s a complete AI driven.” [56]. “In bringing AI in the network side, I can even get patents, customer patents, calling patents, network initiatives.” [80].


Major discussion point

Future vision – AI for all, inclusive protection, and federated learning


Topics

Artificial intelligence | Building confidence and security in the use of ICTs


Federated learning for privacy‑preserving AI

Explanation

Ravi introduces federated learning as a method to train AI models locally while sharing insights across the network, preserving user privacy.


Evidence

“So this is the next concept of what we bring in what we call a federated learning.” [142]. “I just learn your data and I federate over it.” [143].


Major discussion point

Future vision – AI for all, inclusive protection, and federated learning


Topics

Artificial intelligence | Data governance


Agreements

Agreement points

AI-driven fraud protection delivers measurable business and operational results

Speakers

– Vikram Sinha
– Anshuman Kar

Arguments

AI implementation for fraud protection delivered measurable business results: 9% ARPU growth vs 3% industry average and churn reduction from 3.6% to 1.6%


Wisely.ai has protected an estimated $500 million in losses within six months of launch


Summary

Both speakers provide concrete metrics demonstrating the tangible business value and financial impact of AI-driven fraud protection systems, showing that these investments deliver measurable ROI


Topics

Artificial intelligence | Building confidence and security in the use of ICTs | The digital economy


Fraud prevention requires ecosystem-wide collaboration rather than individual solutions

Speakers

– Bipin Preet Singh
– Neha Gutma Mahatme
– Ratan Kumar Kesh

Arguments

Fraud prevention requires ecosystem-wide collaboration as no single entity can control scams alone


Data silos limit visibility as no single institution sees the complete fraud journey across platforms


Customer education and awareness need improvement across the ecosystem to prevent gullible behavior


Summary

All three speakers agree that fraud is an interconnected problem that cannot be solved by individual institutions alone, requiring coordinated effort across banks, fintechs, platforms, and regulatory bodies


Topics

Building confidence and security in the use of ICTs | The digital economy | The enabling environment for digital development


The scale and sophistication of digital fraud has reached crisis levels requiring urgent action

Speakers

– Sanjay Kapoor
– Vikram Sinha
– Ratan Kumar Kesh
– Anshuman Kar

Arguments

Global consumers have lost over $1 trillion to scams, with 65% of Indonesians facing spam or scam weekly


Indosat transformed from viewing fraud as customer complaints to a board-level strategic issue after learning Indonesians lost $5 billion to scams in 2024


Supreme Court identified 54-56,000 crores lost to scams in India, affecting everyone from senior citizens to IIT professors


In India, 70% of scams originate from SMS channels, with 65 billion SMS and 15 billion OTT messages sent monthly


Summary

All speakers acknowledge that digital fraud has reached unprecedented scale with massive financial losses globally, affecting all demographics and requiring board-level attention and strategic response


Topics

Building confidence and security in the use of ICTs | The digital economy


Traditional reactive approaches to fraud detection are insufficient for modern threats

Speakers

– Sanjay Kapoor
– Neha Gutma Mahatme
– A. Robert J. Ravi

Arguments

Digital fraud has evolved from isolated phishing to AI-powered, cross-border, automated operations at industrial scale


Scams involve behavioral journeys that start before payment transactions, with social engineering happening upstream


The goal is building systems where citizens can be 100% confident in network safety


Summary

Speakers agree that modern fraud requires proactive, intelligent systems rather than reactive detection, as threats have evolved to sophisticated, AI-powered operations that begin well before transactions occur


Topics

Artificial intelligence | Building confidence and security in the use of ICTs


Strategic partnerships are essential for effective AI implementation in fraud prevention

Speakers

– Vikram Sinha
– Bipin Preet Singh

Arguments

Partnership approach over vendor relationships is crucial for solving real problems at scale using AI


Individual fraud models trained on company-specific data perform better than industry-level solutions


Summary

Both speakers emphasize that successful AI implementation requires deep strategic partnerships and customized solutions rather than generic vendor relationships or one-size-fits-all approaches


Topics

Artificial intelligence | The enabling environment for digital development


Similar viewpoints

Both telecom leaders agree that the fundamental mission of telecommunications companies has expanded beyond connectivity to include comprehensive customer protection and security as core responsibilities

Speakers

– Vikram Sinha
– A. Robert J. Ravi

Arguments

Telcos’ role has evolved from just connecting customers to providing protection and peace of mind


The goal is building systems where citizens can be 100% confident in network safety


Topics

Building confidence and security in the use of ICTs | Information and communication technologies for development


Both speakers highlight the asymmetric challenges faced by legitimate organizations in fighting fraud, where criminals operate without constraints while defenders must comply with regulations and maintain customer experience

Speakers

– Neha Gutma Mahatme
– Bipin Preet Singh

Arguments

Offensive AI operates unconstrained while defensive AI faces privacy, regulatory, and customer experience constraints


Despite knowing fraud origins and having technology to track perpetrators, law enforcement action remains insufficient


Topics

Artificial intelligence | Building confidence and security in the use of ICTs | Human rights and the ethical dimensions of the information society


Both financial services leaders identify the interconnected nature of fraud where money flows across multiple institutions, making individual prevention efforts insufficient and highlighting the mule account problem

Speakers

– Ratan Kumar Kesh
– Bipin Preet Singh

Arguments

Two main fraud problems: customers being defrauded and accounts being used as mules, with the latter being more troubling due to willing participation for easy money


99% of fraud complaints at MobiQuik involve money stolen from other banks but routed through their platform, highlighting interconnected fraud ecosystem


Topics

The digital economy | Building confidence and security in the use of ICTs


Unexpected consensus

Need for increased transaction friction to combat fraud

Speakers

– Bipin Preet Singh

Arguments

The regulatory response includes making transactions more difficult to add friction and prevent easy fraud


Explanation

It’s unexpected that a fintech CEO would support making transactions more difficult, as this goes against the industry’s traditional focus on seamless user experience. This represents a significant shift in thinking where security is prioritized over convenience


Topics

The enabling environment for digital development | Building confidence and security in the use of ICTs


Privacy-preserving AI through federated learning

Speakers

– A. Robert J. Ravi

Arguments

BSNL is developing federated learning systems where customer data remains local while enabling AI learning across edge data centers


Explanation

It’s unexpected to see a traditional government telecom operator leading in advanced AI privacy techniques like federated learning, typically associated with cutting-edge tech companies. This shows progressive thinking in balancing AI capabilities with privacy protection


Topics

Artificial intelligence | Data governance | Closing all digital divides


AI being used by both fraudsters and defenders

Speakers

– Neha Gutma Mahatme
– Vikram Sinha

Arguments

Offensive AI operates unconstrained while defensive AI faces privacy, regulatory, and customer experience constraints


Partnership approach over vendor relationships is crucial for solving real problems at scale using AI


Explanation

There’s unexpected consensus that AI is fundamentally changing the fraud landscape by being weaponized by criminals, requiring defenders to also adopt AI but within ethical and regulatory constraints. This represents a new paradigm in cybersecurity


Topics

Artificial intelligence | Building confidence and security in the use of ICTs


Overall assessment

Summary

Strong consensus exists on the crisis-level scale of digital fraud, the need for AI-driven solutions, ecosystem-wide collaboration, and the evolution of organizational responsibilities beyond traditional boundaries


Consensus level

High level of consensus with significant implications for industry transformation. All speakers agree that traditional approaches are insufficient and that AI-powered, collaborative solutions are essential. The consensus suggests a fundamental shift in how organizations view their responsibilities in the digital ecosystem, moving from siloed approaches to integrated, proactive protection strategies. This alignment across different sectors (telecom, banking, fintech, platforms) indicates readiness for coordinated action and regulatory support for comprehensive fraud prevention initiatives.


Differences

Different viewpoints

Individual vs. ecosystem-wide approach to fraud prevention

Speakers

– Bipin Preet Singh
– Ratan Kumar Kesh
– Neha Gutma Mahatme

Arguments

Individual fraud models trained on company-specific data perform better than industry-level solutions


Banks can identify out-of-routine transactions using sophisticated rule engines and AI algorithms, but the mule account problem persists


Data silos limit visibility as no single institution sees the complete fraud journey across platforms


Summary

Bipin argues for company-specific models while others emphasize the need for ecosystem-wide collaboration and data sharing to address fraud comprehensively


Topics

Building confidence and security in the use of ICTs | Data governance | Artificial intelligence


Regulatory approach to transaction friction

Speakers

– Bipin Preet Singh
– Vikram Sinha

Arguments

The regulatory response includes making transactions more difficult to add friction and prevent easy fraud


AI implementation for fraud protection delivered measurable business results: 9% ARPU growth vs 3% industry average and churn reduction from 3.6% to 1.6%


Summary

Bipin reports regulators want to add friction to prevent fraud, while Vikram demonstrates that AI can maintain customer experience while providing protection


Topics

The enabling environment for digital development | Building confidence and security in the use of ICTs | The digital economy


Primary source of fraud problem

Speakers

– Ratan Kumar Kesh
– Neha Gutma Mahatme

Arguments

Two main fraud problems: customers being defrauded and accounts being used as mules, with the latter being more troubling due to willing participation for easy money


Scams involve behavioral journeys that start before payment transactions, with social engineering happening upstream


Summary

Ratan focuses on mule accounts as the primary concern while Neha emphasizes upstream social engineering as the root cause


Topics

Building confidence and security in the use of ICTs | The digital economy


Unexpected differences

Law enforcement effectiveness

Speakers

– Bipin Preet Singh
– Ratan Kumar Kesh

Arguments

Despite knowing fraud origins and having technology to track perpetrators, law enforcement action remains insufficient


Customer education and awareness need improvement across the ecosystem to prevent gullible behavior


Explanation

While both acknowledge systemic issues, Bipin is more critical of law enforcement inaction despite known fraud locations, while Ratan focuses more on customer behavior and education as solutions


Topics

Building confidence and security in the use of ICTs | The enabling environment for digital development


Data privacy vs. fraud prevention trade-offs

Speakers

– Neha Gutma Mahatme
– A. Robert J. Ravi

Arguments

Offensive AI operates unconstrained while defensive AI faces privacy, regulatory, and customer experience constraints


BSNL is developing federated learning systems where customer data remains local while enabling AI learning across edge data centers


Explanation

Neha sees privacy constraints as limiting defensive AI effectiveness, while Robert proposes federated learning as a solution that preserves privacy while enabling AI capabilities


Topics

Artificial intelligence | Data governance | Human rights and the ethical dimensions of the information society


Overall assessment

Summary

The main disagreements center around implementation approaches rather than fundamental goals. All speakers agree fraud is a serious ecosystem-wide problem requiring AI and collaboration, but differ on whether solutions should be centralized vs. distributed, company-specific vs. industry-wide, and technology-focused vs. education-focused.


Disagreement level

Moderate disagreement with high consensus on core issues. The disagreements are constructive and focus on tactical approaches rather than strategic objectives, suggesting potential for convergence through hybrid approaches that combine different perspectives.


Partial agreements

Partial agreements

All speakers agree that fraud is an ecosystem-wide problem requiring collaboration, but disagree on implementation – Bipin emphasizes national-level data sharing initiatives, Neha focuses on breaking down data silos, and Ratan emphasizes customer education

Speakers

– Bipin Preet Singh
– Neha Gutma Mahatme
– Ratan Kumar Kesh

Arguments

Fraud prevention requires ecosystem-wide collaboration as no single entity can control scams alone


Data silos limit visibility as no single institution sees the complete fraud journey across platforms


Customer education and awareness need improvement across the ecosystem to prevent gullible behavior


Topics

Building confidence and security in the use of ICTs | Data governance | Capacity development


Both agree on comprehensive AI implementation for fraud prevention, but Vikram emphasizes strategic partnerships while Robert focuses on in-house AI development and federated learning

Speakers

– Vikram Sinha
– A. Robert J. Ravi

Arguments

Partnership approach over vendor relationships is crucial for solving real problems at scale using AI


BSNL is implementing AI across network operations to enable intelligent customer-network interactions and proactive spam/scam prevention


Topics

Artificial intelligence | Building confidence and security in the use of ICTs | The enabling environment for digital development


Similar viewpoints

Both telecom leaders agree that the fundamental mission of telecommunications companies has expanded beyond connectivity to include comprehensive customer protection and security as core responsibilities

Speakers

– Vikram Sinha
– A. Robert J. Ravi

Arguments

Telcos’ role has evolved from just connecting customers to providing protection and peace of mind


The goal is building systems where citizens can be 100% confident in network safety


Topics

Building confidence and security in the use of ICTs | Information and communication technologies for development


Both speakers highlight the asymmetric challenges faced by legitimate organizations in fighting fraud, where criminals operate without constraints while defenders must comply with regulations and maintain customer experience

Speakers

– Neha Gutma Mahatme
– Bipin Preet Singh

Arguments

Offensive AI operates unconstrained while defensive AI faces privacy, regulatory, and customer experience constraints


Despite knowing fraud origins and having technology to track perpetrators, law enforcement action remains insufficient


Topics

Artificial intelligence | Building confidence and security in the use of ICTs | Human rights and the ethical dimensions of the information society


Both financial services leaders identify the interconnected nature of fraud where money flows across multiple institutions, making individual prevention efforts insufficient and highlighting the mule account problem

Speakers

– Ratan Kumar Kesh
– Bipin Preet Singh

Arguments

Two main fraud problems: customers being defrauded and accounts being used as mules, with the latter being more troubling due to willing participation for easy money


99% of fraud complaints at MobiQuik involve money stolen from other banks but routed through their platform, highlighting interconnected fraud ecosystem


Topics

The digital economy | Building confidence and security in the use of ICTs


Takeaways

Key takeaways

Digital fraud has evolved into an AI-powered, industrial-scale threat requiring coordinated ecosystem response rather than individual institutional solutions


Telcos must transform from connectivity providers to protection platforms, with AI-driven fraud prevention becoming a core business differentiator


Real-time AI implementation delivers measurable business results – Indosat achieved 9% ARPU growth vs 3% industry average and reduced churn from 3.6% to 1.6%


The attack surface is interconnected across the digital ecosystem, but current defense mechanisms remain fragmented across institutions


Partnership-based approaches with specialized AI platforms like Wisely.ai prove more effective than vendor relationships for solving fraud at scale


Customer education and behavioral change are as critical as technological solutions in combating sophisticated social engineering attacks


National-level data sharing and intelligence platforms are essential for effective fraud prevention given the cross-border, multi-platform nature of modern scams


Resolutions and action items

BSNL committed to expanding AI implementation across network operations to enable intelligent customer-network interactions


Indosat demonstrated successful deployment of Wisely.ai platform protecting 100 million subscribers with measurable business impact


Industry consensus on need for ecosystem-wide collaboration rather than siloed approaches to fraud prevention


Recognition that RBI’s Digital Payments Intelligence Authority and mule hunter initiatives require industry support and participation


Agreement on implementing federated learning systems where customer data remains local while enabling AI learning across platforms


Unresolved issues

Law enforcement gaps persist despite known fraud origins and available tracking technology


Data sharing limitations across institutions continue to hamper comprehensive fraud detection


Balance between customer experience friction and security measures remains challenging to optimize


Offensive AI capabilities continue to outpace defensive AI due to fewer constraints on malicious actors


International coordination mechanisms for cross-border fraud prevention are underdeveloped


Standardization of fraud prevention protocols across different types of financial institutions


Effective methods to prevent willing participation in mule account schemes for easy money


Suggested compromises

RBI’s approach to add transaction friction to balance ease of use with security requirements


Federated learning models that enable AI training while keeping customer data locally stored


Ecosystem-wide responsibility sharing rather than placing full burden on individual institutions


Graduated response systems that apply different security levels based on transaction patterns and risk profiles


Public-private partnership models for national fraud prevention infrastructure development


Thought provoking comments

That report shows that in 2024 itself, 5 billion US dollar Indonesians have lost. What touched me is these are all middle income, lower income women, elderly women… every Indonesian, 65% of the Indonesians are facing spam or scam on a weekly basis. So that itself was a trigger for me that Indosat being such an iconic brand… Our role is not only to connect. Our role is to also protect my 100 million customer.

Speaker

Vikram Sinha


Reason

This comment reframes the fundamental purpose of telecom companies from mere connectivity providers to guardians of digital safety. The specific statistics ($5 billion losses, 65% weekly exposure) and focus on vulnerable populations (women, elderly) transforms the discussion from abstract business concerns to urgent social responsibility.


Impact

This shifted the entire conversation from viewing fraud as a customer service issue to recognizing it as a core business imperative. It established the moral and business case for massive AI investments and set the tone for discussing telecom companies as protectors rather than just service providers.


I’m a very strong believer of fake it till you make it. So I started talking about AI two years back and I had very little understanding… But within an hour, I understood companies, countries who have been all in and ahead of the curve and who are solving real problem, they have started seeing value.

Speaker

Vikram Sinha


Reason

This brutally honest admission from a CEO about initially not understanding AI while publicly championing it reveals the reality of technology adoption in large organizations. It challenges the typical narrative of confident, all-knowing leadership and shows authentic learning in action.


Impact

This vulnerability opened up more honest discussion about AI implementation challenges. It gave permission for other panelists to discuss their own learning curves and uncertainties, making the conversation more authentic and practical rather than purely promotional.


SCAM is not at a transaction or a payment transaction level, it is a behavioral journey it starts much before the payment really happens… The offensive AI works unconstrained while the defensive AI has constraints of privacy, constraints of regulations, constraints of customer experience.

Speaker

Neha Gutma Mahatme


Reason

This insight fundamentally reframes the fraud problem from a point-in-time transaction issue to a complex behavioral journey. The observation about asymmetric AI capabilities (offensive vs defensive) introduces a sophisticated understanding of the technological arms race.


Impact

This comment elevated the technical sophistication of the discussion and helped explain why traditional fraud prevention fails. It led to deeper exploration of ecosystem-wide solutions rather than individual company approaches, and highlighted the inherent disadvantages faced by legitimate businesses.


99% of whatever scams that our customers complain of are not the money stolen out of MobiQuick, but it is actually stolen out of some other bank and come into MobiQuick. So we are the recipients… one entity cannot control scams. It’s very, very difficult.

Speaker

Bipin Preet Singh


Reason

This reveals the interconnected nature of the fraud ecosystem where companies become unwilling participants in fraud chains. It challenges the assumption that individual companies can solve fraud independently and highlights the blame-shifting that occurs in the ecosystem.


Impact

This observation redirected the conversation toward systemic solutions and shared responsibility. It helped explain why individual AI models and company-specific solutions have limited effectiveness, leading to discussion of national-level coordination and data sharing initiatives.


The fraudster is telling my employees, saying that, can you share with me more data? Don’t worry, I’ll not tell anybody, just give me data, I’ll give you 70,000, I can even pay you 1,50,000 per account.

Speaker

Ratan Kumar Kesh


Reason

This anecdote reveals the sophisticated recruitment tactics of fraudsters and the economic incentives driving the fraud ecosystem. It shows how fraudsters are actively trying to corrupt the very systems designed to protect against them, highlighting the human element in cybersecurity.


Impact

This story brought a visceral reality to the discussion, moving beyond abstract statistics to show the personal and institutional vulnerabilities. It emphasized that technology solutions alone are insufficient without addressing human factors and economic incentives.


Unless we have built a system where we confidently say our citizens that you are 100% safe in my network, our job is not done. And this is possible only when we bring in technology and play it across in a platform which really intelligently builds this network.

Speaker

A. Robert J. Ravi


Reason

This sets an absolute standard for success (100% safety) rather than incremental improvements, and positions network-level AI integration as a national infrastructure imperative. It elevates the discussion from business optimization to citizen protection as a fundamental right.


Impact

This comment provided a visionary endpoint for the discussion, establishing citizen safety as the ultimate measure of success. It helped frame all the previous technical and business discussions within a larger national security and social responsibility context.


Overall assessment

These key comments fundamentally transformed what could have been a typical technology product discussion into a profound examination of corporate responsibility, systemic vulnerabilities, and societal protection. Vikram’s honest admission about learning AI while implementing it set a tone of authentic dialogue, while his statistics about fraud impact established the moral imperative. Neha’s insight about behavioral journeys and asymmetric AI capabilities elevated the technical sophistication, while Bipin’s revelation about cross-institutional fraud flows highlighted systemic interconnectedness. The banking perspective on insider threats and the BSNL vision of 100% citizen safety provided bookends of current reality and future aspiration. Together, these comments shaped a discussion that moved from individual company solutions to ecosystem-wide collaboration, from reactive fraud detection to proactive citizen protection, and from technology implementation to social responsibility. The conversation evolved into a call for coordinated national-level action rather than fragmented individual efforts.


Follow-up questions

How can AI-driven fraud prevention be expanded to protect against data-side scams beyond SMS, particularly on WhatsApp and social media platforms?

Speaker

A. Robert J. Ravi


Explanation

BSNL’s CMD identified that while SMS spam protection is being addressed, the next frontier is protecting users from scams on data channels like WhatsApp and social media, which requires expanding the AI protection horizon.


How can federated learning be implemented at rural edge data centers to protect customer data while still enabling AI-driven fraud protection?

Speaker

A. Robert J. Ravi


Explanation

This addresses the challenge of protecting customer privacy while still leveraging their data for fraud prevention, particularly important for rural customers who may be more vulnerable to scams.


What specific engineering and technical collaboration opportunities exist between telcos and AI platforms for training fraud detection models?

Speaker

Vikram Sinha


Explanation

Vikram mentioned that Indosat wanted to do engineering work with Tanla using their GPU clusters, suggesting there are untapped technical collaboration opportunities that could enhance fraud detection capabilities.


How can the Digital Payments Intelligence Platform initiative be optimized to ensure real-time data sharing across the entire financial ecosystem?

Speaker

Bipin Preet Singh and Audience Member


Explanation

While the government initiative exists, questions remain about its effectiveness in enabling real-time, comprehensive data sharing needed to combat sophisticated, cross-platform fraud schemes.


What mechanisms can be developed to better coordinate law enforcement action against known fraud hotspots and repeat offenders?

Speaker

Bipin Preet Singh and Ratan Kumar Kesh


Explanation

Both speakers noted that fraud origins are often known but enforcement action is lacking, suggesting need for research into more effective law enforcement coordination mechanisms.


How can AI models be trained to detect malicious intent and behavioral patterns rather than just transaction anomalies?

Speaker

Neha Gutma Mahatme


Explanation

Current AI focuses on detecting anomalies but struggles with identifying malicious intent, which is crucial since social engineering happens before the actual transaction.


What standards and protocols are needed for cross-border fraud prevention given that scammers operate internationally?

Speaker

Anshuman Kar


Explanation

As fraud becomes increasingly international while current solutions are largely national, research is needed into international cooperation frameworks and standards.


How can the balance between defensive AI constraints (privacy, regulations, customer experience) and offensive AI capabilities be optimized?

Speaker

Neha Gutma Mahatme


Explanation

The asymmetry between constrained defensive AI and unconstrained offensive AI used by fraudsters needs to be addressed through research into more effective defensive strategies.


What customer education and awareness strategies are most effective in preventing citizens from becoming fraud victims or unwitting accomplices?

Speaker

Ratan Kumar Kesh


Explanation

The ‘mule account’ problem where citizens rent their accounts to fraudsters suggests need for research into more effective education and awareness programs.


Disclaimer: This is not an official session record. DiploAI generates these resources from audiovisual recordings, and they are presented as-is, including potential errors. Due to logistical challenges, such as discrepancies in audio/video or transcripts, names may be misspelled. We strive for accuracy to the best of our ability.