When banks and fintechs race to adopt AI, the hardest question is not which tools to buy; it's figuring out how to get a return on investment without breaking the customer experience.

AI in customer experience, fraud prevention, and back-office operations is moving fast in banking and financial services, and the firms that fall behind risk losing both customers and competitive ground. Tedd Huff, CEO of fintech advisory firm Voalyre and founder of Fintech Confidential, sits down with Mamta Rodrigues, Chief Client Officer of Banking, Financial Services and Insurance at TP, one of the largest employers in the world with over 500,000 people globally. Mamta brings decades of hands-on experience across American Express, MasterCard, Visa, and Synchrony, and she holds a patent, a signal that she has spent real time building products, not just advising on them. The conversation covers practical AI use cases in fraud, collections, and compliance, along with what separates clients who get results from those who stall out after a pilot.

The pressure on banks and fintechs right now comes from two directions at once. Consumer expectations keep rising because people interact with payment products every single day. At the same time, fraud is accelerating. Every time the industry catches up, fraudsters adapt faster and the cycle resets. That means fraud teams, product teams, and customer experience teams are all fighting for resources and attention at the same time. For treasury managers, CFOs, and compliance leaders, this creates a real tension: how do you invest in AI-powered fraud prevention and still deliver a smooth experience that keeps customers loyal?

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The numbers from inside TP's client work tell a clear story. Fifty percent of TP's solutions are now AI-led, with the heaviest concentration in back-office operations like fraud, financial crime, and claims management. Mamta describes a recent deployment of TP's AI blueprint, TP AI fab, layered into an existing client's operations to prevent and predict fraud. The results showed significant improvement in key metrics. On the collections side, predictive analysis now arms agents before a call even starts with propensity to pay, likely timing, expected recovery percentage, and recommended remediation paths. That kind of preparation changes the entire tone of a collections interaction from adversarial to solution-oriented, and the outcome is measurable: increased repayment, stronger loyalty, product expansion, and reduced breakage.

A reasonable skeptic would push back here. Plenty of companies have made big AI promises and delivered underwhelming results, especially when legacy systems hold the data hostage. Mamta acknowledges this directly. Legacy infrastructure has been a persistent problem for her clients. The same data element can have four or five different names across systems, making real-time analysis painfully difficult. The counter to that friction is the shift toward hyperscalers and cloud-based solutions that allow firms to build modular tech stacks. TP takes a partnership-first approach, assembling best-of-breed components like building blocks, aligning them with one or more hyperscalers, and going to market with a combined stack rather than trying to build everything internally. That modular method lets clients move faster without ripping out their entire infrastructure on day one.

One of the clearest signals Mamta uses to gauge whether a client will actually get results versus abandon the effort after a test: the composition of who shows up. When the cross-functional team walks through the door, operations, product, IT, and data leaders together, that's when real progress happens. She describes a design thinking approach where the client provides a problem statement in advance, both sides bring the right people, and in a single day they can shape a solution direction. The typical pattern is that they start with one problem statement and end the session with additional problem statements and new opportunities they had not considered. Clients who send a single department to "explore AI" without bringing the other stakeholders rarely make it past the pilot stage.

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A meaningful shift in the last twelve months is that more clients now openly admit they do not have all the answers. A year ago, the common response was "we'll figure this out inside our own house." Now, firms are actively seeking peer group insights, asking TP what patterns they are seeing across 350+ BFSI clients globally. That willingness to learn from outside their own walls is accelerating collaboration across the industry. Mamta makes a sharp point here: competition should not happen at the infrastructure layer while the entire industry is figuring out how AI will change daily operations. Firms should build the foundation together and compete on the products and experiences they create on top of it.

For fintech leaders watching the fraud space closely, the biggest client ask right now is guidance on fraud tools that deliver speed and efficiency while protecting against increasingly sophisticated threats. Fintechs in particular are using partners like TP as a strategic layer because of TP's supplier-agnostic tech stack, which lets smaller firms stay nimble with their own product moves without locking into a single vendor.

Looking three to five years out, Mamta expects advanced AI and predictive analytics to fundamentally reshape how customer experience operates, powered by stronger data foundations and more mature tech stacks. She predicts continued growth in AI-led back-office solutions, deeper fraud protection capabilities, and a rising focus on elevating talent rather than replacing it. The human factor, she says, will always remain because both the customers and the agents serving them are still people. Her single piece of advice to fintech executives and founders: "Be comfortable with the uncomfortable." The firms that try, pivot, learn, and avoid the belief that they already know everything will be the ones that pull ahead.

This episode is built for operators who want to understand where AI is actually delivering measurable results in BFSI right now, not in theory. Anyone responsible for customer experience, fraud prevention, collections, or compliance strategy will walk away with a clearer picture of what readiness looks like, what a productive client-partner engagement actually involves, and where the highest-impact AI use cases are landing today.

Banks and fintechs are spending heavily on AI, but most still cannot answer one critical question: what is the actual return on investment for customer experience? Mamta Rodrigues, Chief Client Officer of Banking, Financial Services and Insurance at TP, breaks down where AI in fraud prevention and back-office operations is producing real, measurable results right now. Tedd Huff, CEO of fintech advisory firm Voalyre and founder of Fintech Confidential, pushes Mamta on the practical side: how predictive analysis is changing collections calls, why behavioral biometrics catch what passwords miss, and what signals reveal whether a company is ready for meaningful change or just running another pilot that goes nowhere. Mamta shares why the firms pulling ahead are the ones willing to admit they do not have all the answers and partner to move faster. Her one-sentence advice to every fintech executive: be comfortable with the uncomfortable.

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Every mobile transaction generates thousands of hidden data points including gyroscope movement, touch pressure patterns, key press timing, and screen angle behavior that machine learning models use to verify identity. IP address matching combined with geolocation checks can confirm whether the person making a payment is physically located where their device says they are, adding layers of fraud protection most consumers never realize exist.

TP proposes automation first in every client engagement, yet the goal is augmenting agent performance through AI-powered training, quality assurance, and workforce management tools. Mundane tasks like balance inquiries have already moved to apps, while new roles in data analysis, predictive modeling, financial crime investigation, and fraud prevention are growing faster than the positions being phased out.

Banking and payments typically lead BFSI adoption cycles because consumers transact with payment products daily, while insurance interactions are infrequent and purpose-driven. That frequency gap means consumer expectations hit banking and fintech firms first, forcing faster response times and creating pressure that insurance companies eventually absorb as a fast follower.

One of the sharpest product leadership lessons came from spending an entire month using only cash, no cards, no checks, no electronic payments, to understand what consumers actually experience when they lack access to modern payment tools. That hands-on immersion shaped a framework for understanding customer pain points from the inside out, a method still applied today when onboarding new clients by finding internal employees who already use the client's products.

The phrase "so what of the data" reframes the entire conversation around why raw data collection means nothing without a clear connection to personalization, spend analysis, and predictive outcomes. Combining multiple data sources with analytics can reveal buying power, transaction patterns, location behavior, and propensity to pay, turning passive information into active intelligence that drives customer engagement and retention.

Complex enterprise sales involving operations, product, and executive teams require more than technical specs to move forward, and framing solutions around a clear North Star with a human impact story accelerates buy-in. Using a collections call as an example, the narrative centers on saving a customer relationship rather than recovering a balance, which reframes cost of acquisition against breakage and makes the ROI case emotionally and financially persuasive.

A year ago, most banking clients told TP they would solve AI and CX challenges internally within their own teams and systems. In the last twelve months, that posture has shifted sharply toward requesting peer group insights, consortium-style knowledge sharing across 350+ global BFSI clients, and collaborative problem solving that treats the current wave of change as an industry-wide learning curve.

Three years of living and working in India reinforced that cultural context directly affects how customers respond to service interactions, communication styles, and engagement approaches across different regions. Global CX strategies that ignore cultural layers risk delivering a technically sound but emotionally flat experience, which is why regional adaptation matters as much as the tech stack powering the interaction.

Beyond standard two-factor and three-factor authentication, financial services firms are now layering behavioral biometrics that track how a person physically handles their device during a transaction. Screen touch patterns, movement signatures, and Face ID verification create a composite identity profile that runs silently behind every interaction, catching anomalies that traditional password-based security would miss entirely.

Cross-functional leadership across global teams starts with something as simple as asking a new direct report which communication channel they prefer, whether that is Viber, WhatsApp, text, or another platform. That small signal of respect sets the tone for a people-first management approach where multiple perspectives are actively solicited, because the operating principle is that one brain is never as effective as seven or eight working together.

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Sending one department to evaluate AI or data analytics tools is how pilots die quietly after 90 days. Get your operations lead, product owner, IT or data leader, and digital officer in the same room with one shared problem statement before you commit budget. That combination forces the real blockers to surface early, things like legacy system constraints, rule adjustments, and use case selection, so you can design around them instead of discovering them after you have already spent the money.

The fastest way to understand a customer's pain is to become one. Before pitching a solution or onboarding a new client, find people inside your own organization who already use that client's product and pull them into the conversation. You will learn more about friction points, feature gaps, and real user behavior in one week of hands-on product use than in six months of reading market research decks.

Collections calls where the agent goes in blind are a waste of everyone's time and a fast track to customer breakage. Feed your agents predictive data before they pick up the phone: propensity to pay, likely timing, expected recovery amount, and recommended remediation options. When agents lead with a solution instead of a demand, repayment goes up, loyalty increases, and customers expand into additional product lines instead of closing accounts.

Trying to build your entire AI and data infrastructure in-house while legacy systems are still running is how you burn two years and ship nothing. Assemble a tech stack from best-of-breed partners, align it with a hyperscaler, and go to market with a combined solution that plugs into your existing cloud environment. Fintechs already using this approach are moving faster because they stay supplier-agnostic and can swap components without rebuilding the whole foundation.

Every bank and fintech fighting to build proprietary AI foundations from scratch right now is solving the same problem separately and wasting resources in the process. Share learnings at the infrastructure level through peer groups, consortium models, and partner networks, then compete fiercely on the products and experiences you build on top of that shared foundation. The firms pulling ahead are the ones treating the current wave as an industry-wide building phase, not a zero-sum land grab.

LinkedIn: https://www.linkedin.com/in/mamtarodrigues/

Website: https://www.tp.com

LinkedIn: https://www.linkedin.com/company/teleperformance

BFSI Services: https://www.tp.com/en-us/industries/banking-and-financial-services/

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Mamta Rodrigues is Global Chief Client Officer of Banking, Financial Services and Insurance (BFSI) at TP, the world's largest digital business services company. Over the past 30+ years, she has held senior leadership roles at Synchrony Financial, Visa, MasterCard, and American Express, providing strategic direction across payments, product development, and customer experience on a global scale. Her expertise focuses on turning AI, data analytics, and automation into measurable outcomes for banking and fintech clients. A World Bank advisory group member and published thought leader, Mamta co-chairs TP Women and was named a Top Women Leader of New York in 2024. She holds an MBA from NYU Stern School of Business and a BA from Boston University, and is known for her people-first leadership style, design thinking approach, and deep understanding of fraud prevention, collections, and financial services operations.

Tedd Huff is CEO of Voalyre, a fintech advisory firm, and founder of Fintech Confidential. Over the past 25+ years, he has contributed to fintech startups as an Advisory Board Member, Co-Founder, and Chief Experience Officer, providing strategic and tactical direction for global companies. His expertise focuses on growth while delivering process improvements and user experience-driven value to simplify the complexity of payments. As host and executive producer of Fintech Confidential, Tedd brings entertaining and informative content focused on fintech industry insights, market trends, and stories from fintech leaders, thinkers, and doers. He is a recognized thought leader and U.S. Army veteran known for making complex financial technology approachable and engaging through his conversational storytelling style and deep understanding of global payments, cross-border transactions, and payment localization.

Fintech Confidential is produced by DD3 Media.

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