21 GenAI in Banking & Finance: Post 1 Fraud and Risk Management

How GenAI Is Redefining Fraud Detection  From Rules to Real-Time Reasoning

Series Overview

GenAI in Banking & Finance – Powered by Your Voice

Over the last 20 weeks, I’ve had the privilege of exploring AI/ML, Generative AI, Prompt Engineering, and hands-on experiments with open-source LLMs on #TechToTransform. The response from the community has been incredible — and it’s shaped what comes next.

After running a poll to understand where your curiosity and priorities lie, the message was clear:

1. Fraud & Risk Management led with 53% of the vote
2. Followed by Innovation & Future-Readiness at 28%
3. Then Internal Operations Automation (13%)
4. And Compliance & Governance (6%)

With that in mind, I’m launching a new series:
“GenAI in Banking & Finance: Fraud and Risk Management”

In this series, we’ll dive deep into how Generative AI is transforming the financial world — not just hypothetically, but through real-world applications. From combating fraud to building intelligent risk models, from innovating customer experience to enhancing operational efficiency — this is where AI meets action in finance.

You asked for insights. Now we build them together.

Introduction

India’s banking sector — and indeed, financial ecosystems across the globe — are confronting a new kind of fraud epidemic. This isn’t just about large, high-profile scams. The true crisis lies in the volume, frequency, and evolving complexity of digital fraud that now targets everyday consumers at scale.

Recent research reveals that digital financial frauds in India totaled ₹4,245 crore between April 2024 and January 2025 — with UPI-based scams being a major contributor. And while part of the rise in reported frauds stems from reclassification and reinvestigation of old cases, the real concern is the sustained rise of real-time, low-value scams that bypass traditional monitoring.

One of the challenges lies the Unified Payments Interface (UPI) — a transformational innovation in India’s fintech landscape, now processing over $3 trillion worth of transactions annually. But with that scale comes risk. The ease of UPI has made it a prime target for fraudsters using everything from basic phishing schemes and SIM card cloning to advanced, AI-generated scams.

Despite efforts — public awareness campaigns, static fraud rules, and heuristic ML models — banks struggle to keep up. Here’s why:

• Evolving social engineering tactics that adapt faster than system updates

• New fraud typologies in micro-transactions that evade value-based thresholds

• Scalable abuse of open systems that rigid filters can't catch in real-time

Most legacy fraud systems are based on rule engines or batch-trained ML models — which:

• Analyze transactions in isolation

• Require manual feature engineering and retraining

• Can’t adapt fast enough to novel fraud scenarios

• Often trigger high false positives and customer friction

The solution lies not in more rules — but in more intelligence.

We need fraud detection systems that are adaptive, learning-based, context-aware, and capable of reasoning through anomalies — in real time and at scale.

This is where Generative AI (GenAI) steps in — not as a plug-and-play fix, but as a paradigm shift. Unlike legacy systems that simply flag “what looks wrong,” GenAI can understand why it might be wrong — using patterns, context, behavior history, and multi-modal inputs.

In this series, we’ll explore how GenAI is redefining the future of fraud detection — from rigid filters to adaptive, real-time intelligence. We'll look at limitations of current systems, share real-world examples, and showcase how GenAI can power scalable, anticipatory fraud defense for the next decade.

Let’s begin by unpacking why the traditional approaches are struggling — and what makes GenAI fundamentally different.

Why Traditional Fraud Detection Falls Short

For years, banks and financial institutions have relied on a familiar playbook: hardcoded rules, human-curated thresholds, and pre-trained machine learning models. It worked — when fraud was simpler, slower, and easier to spot.

But today’s threats are faster, subtler, and often invisible to systems that weren’t designed to think — only to follow instructions.

Consider the common rule: “Block withdrawals over ₹1,00,000.”
Yes, it can catch some fraud. But it also penalizes legitimate customers — triggering false positives for business purchases, emergency payments, or festive spending. These rigid filters operate without nuance, often undermining trust rather than protecting it. 

What’s worse, they lack context. A transaction may look suspicious on the surface — a new device, a late-night payment, an unfamiliar location — but without a full understanding of the user’s history or behavior, the system makes blind guesses. 

Fraudsters know this. They adapt fast, tweaking just enough to stay below the radar: splitting amounts, shifting transaction times, mimicking normal behavior. And every new tactic means another round of manual rule updates — a process that’s slow, reactive, and increasingly ineffective. 

Even traditional machine learning — while a step up — struggles to keep pace. It relies on large volumes of labeled data, periodic retraining/ recalibration, and fixed features. By the time models are retrained to detect a new pattern, fraudsters have already moved on.

And then comes the issue of scale.

In a world of billions of micro-transactions, legacy fraud systems choke. They can’t analyze data fast enough. They introduce latency, flag too late, or hand off too many alerts to human investigators already overwhelmed. 

The result? A brittle, reactive defense that’s too slow for today’s adversaries — and too rigid for real-time finance.

We don’t just need better filters.
We need systems that learn, reason, and adapt on the fly.

That’s the promise of Generative AI.

What Generative AI Brings

Illustration: AI augments fraud detection by analyzing transactions in real-time. 

Generative AI (GenAI) introduces an adaptive, contextual layer to fraud defense. Unlike fixed rules, modern GenAI models learn from vast data: they can identify new fraud patterns on the fly and even simulate attacks to prepare defenses bai.org. These models naturally encode context – time of day, user device, transaction history, merchant type, etc. – so they flag a transaction as suspicious because of how it deviates from context, not just how big it is. GenAI systems can be fine-tuned quickly: a handful of new fraud examples (or synthetic frauds) can update the model almost immediately. And being transformer-based, they scale to analyze millions of micropayments with millisecond latency ibm.com.

• Dynamic Pattern Learning. GenAI doesn’t just match known templates; it discovers evolving anomalies. For example, if credential‑stuffing login attempts suddenly spike in a region, a GenAI model trained on multi-bank data could flag the pattern days before it’s recognized as a known scampacificdataintegrators.compacificdataintegrators.com. Likewise, it can generate synthetic fraud scenarios (via GANs) to anticipate emerging schemesbai.org.

• Contextual Behavior Analysis. GenAI evaluates each event in full context. A ₹5,000 grocery payment at noon and the same amount at 3 AM on a new device would look identical to a rule‑checker, but a GenAI model would see the odd time+device combination and question it. These systems can ingest multi‑modal signals – transaction trails, device ID, geolocation, even chat or voice logs – building a 360° fraud profile. Graph Neural Networks, for instance, can analyze transaction networks and spot subtle fraud rings that rules missibm.com.

• Rapid Adaptation. When fraudsters innovate (e.g. AI‑generated phishing calls or social engineering), GenAI adapts quickly. Banks can fine-tune models on small batches of new fraud data – real or synthetic – to catch novel attacks almost instantly. Contrast that with rule engines, which need manual rule “reverse engineering” for each new scammerchantriskcouncil.org. In practice, mature AI pipelines automate retraining, letting models evolve alongside fraud tactics.

• Massive Scale & Speed. GenAI models ingest and score enormous transaction volumes in real time. For example, Mastercard’s AI‑driven “Decision Intelligence” platform analyzes over 160 billion transactions annually and assigns risk scores in millisecondsgovernment.economictimes.indiatimes.com. Modern AI architectures inherently parallelize: the cost of scanning 100,000 events versus 1,000 is trivial (just a larger cloud bill), whereas rule engines require heavy manual augmentation as load growsmerchantriskcouncil.org. In sum, AI-powered systems achieve real-time fraud scoring that legacy filters cannot match.

• Proactive Threat Simulation. A key advantage of GenAI is anticipation. Banks are now using synthetic fraud data to train their systems. By creating simulated fraud scenarios, AI models learn to recognize future scams before they occurbai.org. This “practice ahead” approach – e.g. generating hundreds of fake phishing audio samples or mule‑account schemes – helps the system stay ahead of clever attackers.

Real-World Case: UPI Fraud at Scale

The promise of GenAI is most stark where fraud volume is highest. India’s UPI network is a case in point: it settled over $3 trillion last yearciso.economictimes.indiatimes.com, but also witnessed a surge of scams. Between April 2024 and Jan 2025 India saw about 2.4 million digital fraud incidents with ₹4,245 crore lostm.thewire.in. Conventional fraud rules will typically catch only large anomalous transfers, missing the many small “drip withdrawals” that compound losses. GenAI, by contrast, can recognize behavioral anomalies in real time. For instance, an AI model would flag a series of dozen ₹100 transactions to the same account at odd hours as suspicious (combining timing, device history and unusual transaction count) – even if no single amount exceeds a threshold.

Banks and providers are already deploying AI in this arena. The Reserve Bank’s “MuleHunter.AI” uses AI to spot mule accounts used in fraud ring withdrawalsgovernment.economictimes.indiatimes.com. NPCI has launched a federated GenAI pilot across banks to share fraud patterns safelygovernment.economictimes.indiatimes.com. These efforts complement real-time monitoring: combining GenAI models with continuous surveillance means suspicious transactions can be halted the instant they appearbai.org. In practice, institutions often use hybrid approaches: deterministic rules and human analysts handle clear-cut cases, while GenAI manages the fuzzy frontier of evolving threats.

Challenges & Considerations

GenAI is not a magic wand and brings its own issues. Model drift remains a concern: fraud tactics keep changing, so AI models need ongoing retraining to avoid obsolescencemerchantriskcouncil.org. There is also bias risk: if training data underrepresents certain populations or regions, AI may unfairly flag them as high‑riskibm.com. Crucially, black‑box models demand explainability – regulators insist banks be able to justify why a transaction was blocked. Explainable AI techniques must be layered on top to allow auditing of AI decisions.

Privacy and governance are other factors. GenAI needs large, high-quality data; federated learning can help share insights without moving raw data (as in the NPCI pilotgovernment.economictimes.indiatimes.com). Yet stricter data laws mean institutions must guard how much customer data is fed into AI. Finally, fraudsters themselves use GenAI – crafting more convincing deepfake calls or phishing lures – so defenders must be equally sophisticated.

Looking Ahead

The fraud fight is moving toward multi-modal, proactive intelligence. Future systems may fuse transaction data with video KYC, voice biometrics and chat analysis to authenticate users. Cross-industry AI (federated learning across banks and payment networks) will spot patterns one bank alone never could. Some researchers even talk about AI “reasoning agents” that predict fraud waves before they happen by simulating attacker behavior.

One thing is clear: static rulebooks will no longer suffice. Generative AI transforms fraud detection from a rear‑guard alert system into a real-time reasoning engine. It not only flags anomalies but can explain and adapt to them pacificdataintegrators.com. In a world where fraud moves at digital speed, such adaptive intelligence is the only way to stay a step ahead.

Conclusion

Fraud is no longer just about bad transactions — it’s about inconsistent behavior and broken patterns. GenAI doesn’t just detect anomalies — it explains them, reasons through them, and learns from them.

In a world where fraud moves fast, adaptive intelligence is the only way to stay ahead.

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Coming Up Next

Please let me know in comment section if you would like to next post with python fraud detection code by leveraging 100% opensource LLMs?

✍️ Author’s Note

This blog reflects the author’s personal point of view — shaped by 22+ years of industry experience, along with a deep passion for continuous learning and teaching.
The content has been phrased and structured using Generative AI tools, with the intent to make it engaging, accessible, and insightful for a broader audience.