Challenges of AI in Fintech

Your AI project will succeed or fail before a single model is deployed. The critical decisions happen during vendor selection — especially in fintech where the consequences of poor implementation extend beyond wasted budgets to regulatory exposure and customer trust. Financial institutions have always excelled at vendor risk management. The difference with AI? The risks are less visible and the consequences more profound. After working on dozens of fintech AI implementations, I've identified four essential filters that determine success when internal AI capabilities are limited: 1️⃣ Integration Readiness For fintech specifically, look beyond the demo. Request documentation on how the vendor handles system integrations. The most advanced AI is worthless if it can't connect to your legacy infrastructure. 2️⃣ Interpretability and Governance Fit In financial services, "black box" AI is potentially non-compliant. Effective vendors should provide tiered explanations for different stakeholders, from technical teams to compliance officers to regulators. Ask for examples of model documentation specifically designed for financial service audits. 3️⃣ Capability Transfer Mechanics With 71% of companies reporting an AI skills gap, knowledge transfer becomes essential. Structure contracts with explicit "shadow-the-vendor" periods where your team works alongside implementation experts. The goal: independence without expertise gaps that create regulatory risks. 4️⃣ Road-Map Transparency and Exit Options Financial services move slower than technology. Ensure your vendor's development roadmap aligns with regulatory timelines and includes established processes for model updates that won't trigger new compliance reviews. Document clear exit rights that include data migration support. In regulated industries like fintech, vendor selection is your primary risk management strategy. The most successful implementations I've witnessed weren't led by AI experts, but by operational leaders who applied these filters systematically, documenting each requirement against specific regulatory and business needs. Successful AI implementation in regulated industries is fundamentally about process rigor before technical rigor. #fintech #ai #governance

Why are fintechs having such a hard time with AI & customer acquisition? I’ve spent the last few months deep in the NYC jungle - selling, analyzing, absorbing every conversation. 💡 The best investment you can make as a founder isn't always tech - it's geography. Being physically where your clients are changes the game. In fintech, that's New York. Period. The density of decision-makers, the pulse of the market - it all happens there. So what’s actually making AI fintech go-to-market so hard? Let’s break it down: 1. Everyone’s waiting for Co-pilot to become Gandalf. Prospects have it. It’s only partially effective in specific cases. 2. Enterprises are still building the runway as the AI plane lands. Most don’t have a clear process for onboarding AI vendors yet. So if you're selling in, you're not just the solution, you're also helping them define the steps to get there. 3. There are no agreed-upon benchmarks for AI models. "Which model is better?" "Well... it depends." 4. Trust is the real currency, and it’s in short supply. In finance, just like in healthcare, trust isn’t optional, it’s foundational. Even if you’re fully vetted, there are still many lingering questions like: “Is my data helping just me… or also training your next client’s model?” 5. All hype, no habit. Plenty of C-suite enthusiasm. But ask the mid-levels and teams? Crickets. AI doesn’t work if no one uses it. Adoption = hard. AI in fintech isn’t just a tech problem - it’s a human, trust, and timing problem. Curious to hear how others are navigating this maze, share your thoughts or GTM stories. #fintech #AI #startups #gtm #sales

Sometimes AI Gets It Wrong Does this face look familiar? It doesn’t to me either. That’s because it’s not me. It’s an AI-generated version of “me” — based on a photo and vague prompts. And in many ways, that’s exactly how Generative AI is being treated across the financial services industry right now. Over the past week, I met with more than 20 clients and prospective clients — from New York to Florida, San Francisco to London, and Germany. The conversations were energizing, and the message was clear: AI is everywhere — and nowhere at the same time. It’s being talked about in boardrooms and budget meetings. But true, scaled adoption? Still lagging. Not because of the lack of ambition — but because of real, tangible obstacles. Here are the five themes that consistently surfaced across those client conversations: • Data debt is real. Organizations are drowning in data but still lack the foundation to use it effectively — cleanly, securely, and contextually. • The “how” of GenAI isn’t clear. Everyone sees the potential, but use cases are fragmented and integration into daily workflows is still immature. • Governance is keeping leaders up at night. Legal, compliance, and regulatory frameworks for AI are still being built in-flight — creating risk aversion and uncertainty. • Transformation is too often surface-deep. Front-end digital experiences may look slick, but without back-end modernization, the value gets lost in translation. • Hallucinations and AI quality risks are real. Clients are skeptical of GenAI’s reliability — especially for customer facing engagement, and for good reason. AI that generates confidently wrong answers (like the photo below) can damage brand trust if not governed, tuned, and supervised carefully. At Genpact, we believe the next generation of financial services isn’t about layering AI on top of what exists — it’s about reimagining what’s possible, front to back. This moment reminds me of when I was working in banking in Barcelona in the early 2000s. Back then, internet banking felt novel — even suspicious. Very few were using it. But a handful of us could see the shift coming. Within five years, online banking became a norm, not a novelty. GenAI is at a similar crossroads today. We’re not debating if it will change the industry. We’re deciding who will shape how it does. So no, that photo isn’t me. But the conversations? Those were very real. And they’re shaping what comes tomorrow, today. Lisa Galione Asim Burman Karun Aggarwal Prakash Chacko Kavitha Shankaran Sachin Pai Samir Saurav Priyanka Gaur Manish Nayar Alwin Bathija Deepika Singh Fred Peters Anant Shah Avjinder Singh Bains Radhika Bangaru Venkatachalam Narayanan Alex Bray Satish Acharya #GenAI #SometimesAIGetsItWrong #FinancialServices #Transformation #ClientFirst #DigitalBanking #ExecutiveInsights #Leadership #Innovation #Genpact

From Vineet Jain: Historically, forecasting, budgeting and variance analysis activity heavily rely on manual efforts and reliance on historical data. With the changes in markets and complexity, the need for more agile and data-driven approaches has become paramount. AI can process information from diverse sources that identify hidden trends and generate predictions above human capabilities, but only if you solve... COMMON CHALLENGES AND LIMITATIONS OF AI-DRIVEN FINANCIAL ANALYSIS ➡ Data Quality and Quantity: In financial analysis, data quality and quantity are critical, and AI models heavily rely on data for accurate predictions. Inaccurate or incomplete data can lead to flawed outcomes, insights and predictions. Additionally, AI models require a significant amount of historical data to train effectively, and this large dataset could be a limitation for several businesses. ➡ Model Overfitting: Overfitting occurs when an AI model performs exceptionally well on the training data but, due to exceptional transactions, fails to generalize this new, unseen data. This can happen when the model captures noise or anomalies in the training data, and the new data is widely skewed. Financial data often contains noise due to extraordinary and time-specific transactions, and without careful regularization and validation, AI models can provide misleading results. ➡ Volatility and Uncertainty: Financial markets are inherently volatile and subject to sudden shifts due to black swan conditions, economic events or geopolitical factors. AI models might struggle to accurately predict extreme events or abrupt changes that fall outside the pattern of historical data. ➡ Bias and Interpretability: Biases in historical data can lead to biased predictions and calculation of financial forecasts. Many AI models, particularly deep learning algorithms, operate as “black boxes,” meaning their decision-making process is complex and challenging to understand. Understanding why a model made a particular prediction is crucial for risk assessment and compliance with regulatory standards, and the biased nature impacts the confidence in the forecast. ➡ Human Expertise and Judgment: While AI can process vast amounts of data, human expertise and judgment remain invaluable. AI may not provide the same level of analytical capability that humans have in particular situations. These financial decisions and situational nuances might be a struggle for AI models. ...for the rest of Vineet's list, check out the full article here: https://lnkd.in/gUJVSmf3

AI is already disrupting finance as we know it—and I may not have a crystal ball, but I’m optimistic about the potential to change consumer fintech for the better ✨ Still, we need to be mindful that the road ahead is largely unpaved and challenges are inevitable. As fintech products work to incorporate AI, there are 3 bumps I’m most concerned about: 1️⃣ Lending could become more biased. Good on fintech companies for using expanded data analysis & ML to reduce biases associated with traditional credit scoring—and promote financial inclusion in the process. But if we’re not careful, bias could also be built INTO new credit scoring models. For example, using zip codes as a proxy for socioeconomic status can perpetuate racial and economic biases. This is not a new phenomenon, but it could be harder to unearth without transparency into the algorithms and training data itself. 2️⃣ Fraud could get worse before it gets better. Bad actors could exploit generative AI to impersonate people we trust and coax sensitive information (think: social security numbers) from loved ones using voice cloning. The Economist suggests this is already happening, citing an emotional testimony given at a US Senate subcommittee hearing last month 😱 Zelle is under government pressure to solve similar challenges with fraudulent transactions—and by implication, so are the banks they work with. With the FedNow launch in the last week, there is cause for hope. But I have a hunch fraud will likely get worse before it gets better—and we’re looking to identity verification apps for a solution. 3️⃣ We don’t know how the SEC will regulate this space. The SEC regulates the provision of investment advice in the US, and firms who offer such advice for compensation are traditionally required to register as “investment advisors.” But will fintech platforms that provide personalized investment advice be exempt from these regulatory requirements? How about other ChatGPT-like platforms where people may also go for financial advice? There’s a lot of room for interpretation. If you’re a fintech, the race to transform the industry using AI is happening—whether you’re in it or not. And if you want your product to come out ahead? Remember to hold space in your design for HUMAN behavior, preferences, and biases. After all: your users aren’t AI’s—they’re human beings 😉 Link in comments for our hot takes on AI disruption—including where we think the industry is headed and who we’re watching with bated breath 👇 #finance #ArtificialIntelligence