Leaders Tech Talk: How AI Is Redefining Fraud Prevention in Finance: Insights from a Tech Leader

Banks and payment platforms, both in India and around the globe, are making strides in convenience but are struggling to maintain trust. With the rise of digital transactions, UPI, mobile wallets, and instant credit, customers are enjoying a smoother experience. However, these same advancements have also paved the way for fraud to escalate at an alarming rate. In FY2024, high-value cyber-fraud cases in India surged dramatically, with regulators and the media highlighting a significant uptick in incidents and substantial losses. This isn’t just a minor compliance issue anymore; it’s a serious business risk that threatens customer trust and regulatory relationships.

Fraud in the financial services sector has always been a moving target, constantly adapting to new innovations introduced by banks. In the past, identifying fraud was relatively straightforward: cheque forgeries, stolen cards, and basic phishing scams followed recognizable patterns. Banks relied on manual reviews, rule-based systems, and investigations after the fact. While these methods had their flaws, they were effective in a time of lower transaction volumes, fewer digital interactions, and less sophisticated criminals.

Now, we find ourselves in a different landscape. Digital banking is thriving, and real-time payments are the standard; financial services are operating at an unprecedented pace and scale. Unfortunately, so are the fraudsters. They’re now using advanced tools—including AI—to stay ahead of traditional defenses. Tactics like deepfake impersonations, mule accounts, and AI-driven phishing are making fraud subtler, widespread, and challenging to detect.

The numbers are sobering: Deloitte predicts synthetic identity fraud could cause $23 billion in global losses by 2030, while PwC reports that over half of financial institutions have faced a cyber-fraud attempt in the past two years. The shift from overt fraud to covert fraud renders legacy controls inadequate. It calls for more than incremental improvements. It demands a new set of tools and strategies that can match the scale, speed, and sophistication of modern fraud. That’s where Artificial Intelligence (AI) is proving to be a game-changer.

What Changed and Why Old Controls Fail 

1. Volume + Velocity: Fraud used to be a minor issue, but now it’s happening at lightning speed, with millions of events occurring every single day. Traditional rules and batch analytics just can’t keep pace.
2. Smarter Adversaries: Today’s fraudsters are getting clever, using social engineering, synthetic identities, and even generative AI to create convincing scams, like voice clones and realistic fake documents.
3. First-Party & Insider Risks: It’s not just about external fraud anymore; institutions also have to deal with friendly fraud, authorized push payment (APP) scams, and insider collusion—all of which need a keen understanding of the context.
4. Regulatory Pressure: Regulators are pushing for quicker detection, better customer protection, and clear audit trails—putting pressure on banks to modernize or face penalties and damage to their reputation.

Why AI is Not Optional

Globally, financial institutions are really stepping up their game with AI to tackle the challenges they face. Recent industry surveys reveal that there's a swift uptake of machine learning and GenAI tools in fraud detection, and the results speak for themselves: lower fraud losses, quicker detection times, and a significant reduction in the time spent on investigations.

• IBM points out that both supervised and unsupervised machine learning models can analyze huge datasets to uncover patterns that human analysts might miss.
• PwC highlights that AI-powered detection systems significantly reduce false positives, which boosts both efficiency and accuracy.
• Forbes emphasizes AI's expanding role in anti-money laundering compliance and managing payment risks.
• Fintech experts (CTO Magazine) stress that AI has evolved beyond a supportive role—it now needs to function in real time, adapt on the fly, and scale effortlessly.

How Indian Banks Are Using Real-Time AI to Combat Fraud

India’s banking sector is undergoing one of the fastest digital transformations globally. With UPI now handling over 14 billion transactions each month (NPCI, 2025), the sheer scale makes fraud detection both essential and challenging. Traditional rule-based systems simply cannot keep pace.

Here’s how banks are responding with AI-driven defenses:

• AI-Powered Transaction Scoring
  Banks now deploy engines that assess card swipes, IMPS, NEFT, and UPI transactions in real time. These systems blend behavioral analytics, device intelligence, and geolocation data to score each transaction within milliseconds—blocking suspicious activity before it goes through.
• AI-Assisted Security Operations Centers (SOCs)
  Machine learning filters thousands of alerts daily, surfacing only the high-risk anomalies for investigators. This hybrid approach slashes false positives and frees analysts to focus on complex fraud cases.
• Deep Learning for Pattern Recognition
  Neural networks and graph analytics help uncover hidden fraud networks, mule accounts, and coordinated scams. These models excel in exposing synthetic identity rings that legacy systems miss.
• Customer Awareness Powered by AI
  Technology alone isn’t enough. Banks are deploying AI-driven chatbots and voice assistants to educate and alert customers about phishing attempts, suspicious logins, and unsafe payment practices. Initiatives like “Vigil Aunty” show how banks are combining prevention with awareness.

Fraud detection has shifted from being a behind-the-scenes task to a real-time, proactive defense woven into every digital transaction—whether a QR code payment, online shopping, or cross-border remittance.

Roadmap: Leading with Insight, Trust & Agility

The road ahead calls for a well-organized approach:

• Conduct a fraud maturity audit and bring together telemetry from all systems.
• Experiment with hybrid AI scoring on high-risk transactions (like UPI and ACH).
• Broaden your coverage, incorporate behavioral biometrics, and link up with NPCI and cross-bank intelligence feeds.
• Implement SOAR playbooks for quick containment and run adversarial tests to put AI models to the test.
• Make sure there's explainability, maintain audit trails, and retrain to address bias and drift.
• Promote customer education and collaborate with regulators to build trust across the entire ecosystem.

Conclusion: Building a Resilient Future

Fraud in finance has transformed from a series of isolated incidents into a sophisticated, AI-driven network. In response, Indian banks & financial institutions are stepping up by implementing real-time, AI-powered systems that can detect, adapt, and neutralize threats on a large scale—everything from scoring transactions in milliseconds to using deep learning models that trace fraud networks.

However, relying solely on technology isn't the ultimate solution. The true power comes from blending AI with human oversight, customer awareness, and regulatory cooperation. As digital payments continue to rise and fraud tactics evolve, Indian financial institutions need to encourage collaboration among institutions, regulators, and customers to create a genuinely resilient financial ecosystem.

The journey ahead will require agility, transparency, and a strong sense of ethical responsibility. While fraudsters will keep innovating, so will we. With AI serving as both a protective shield and a strategic partner, Indian banking sector gets more equipped than ever to protect not just transactions, but also the trust and confidence that are essential to our digital economy.

• By Jeetesh K, Senior Vice President of Technology, HDFC Bank

Team IndiaIT360

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