AI in fraud detection: Fighting financial crime with real-time analytics

Priya Mehra, a 34-year-old communications professional, fell prey to a real estate scam while searching for a house online. After finding a listing she liked, she contacted the owner, who requested a ₹2,000 ‘visiting charge’. Under pressure to secure a home quickly, she transferred the amount. The owner later asked for a refundable down payment. Despite having doubts, Priya went ahead with the transaction due to the urgency of her situation, only to eventually realise that she had been duped.

In today’s hyper-connected world, digital payments have become second nature. In India alone, over 15 billion transactions are processed through the Unified Payments Interface (UPI) each month (as of March 2025). India’s digital financial landscape is poised for significant growth, with its fintech market valued at $111.14 billion in 2024 and expected to grow to $421.48 billion by 2029, showcasing a remarkable compound annual growth rate (CAGR) of 30.55 per cent.

That’s great news, but it also presents the problem of tackling fraud on a broader level. While more and more people feel financially included, the sheer scale and speed of payments also mean that new avenues open for fraudsters to prey upon unsuspecting people.

To stay ahead, financial institutions must move beyond static fraud rules and build something more adaptive — like a real-time, financial immune system powered by artificial intelligence (AI).

Traditional fraud detection relies on predefined patterns. But today’s fraudsters are using AI and crafting synthetic identities, deepfake Know Your Customer (KYC) documents, and targeted phishing that evolves faster than rulebooks can keep up. New video AI models are improving very rapidly, and video KYC (vKYC) solutions are struggling to keep up.

False positives

Thus, inefficiencies increase and leave behind a high rate of false positives. That means real fraud still slips through, while legitimate customers get blocked: a double failure. It clogs compliance teams, slows transactions, and chips away at user trust.

This is where AI truly shines. We’re living in an age where more data will be generated in the next three years than in the entirety of human history. For AI, this is like gold, because AI thrives on data, whether transactional, behavioural, and even alternative sources like UPI flows or device signals to detect patterns no human could catch. In fact, the banking, financial services, and insurance (BFSI) sector’s adoption of AI is already at 68 per cent in India, showing its potential to transform banking, lending, payments, and risk management. This huge market is projected to grow to $3.9 billion by 2028.

The good news with AI is that, unlike rigid rules, AI systems adapt and learn continuously. This adaptive layer creates what’s often called a ‘data moat’, which is a defensible edge that widens with each insight.

AI is already automating routine processes like data reconciliation and risk checks, and this has been cutting down on manual work and delays. AI has the capability to detect nuanced signals like how someone is holding a device or whether a voice recording is authentic. AI also has the ability to detect advanced threats like deepfake identity fraud, leading to fewer false positives and more accurate results.

Every case of confirmed fraud, every mistake, and every near-miss feeds the AI system. And the system learns, improves, and adapts. This feedback loop is what makes AI models better over time, because it reduces friction for genuine users. It also helps compliance teams focus on the real risks. The results of such a system truly speak for themselves. Mastercard’s AI-powered solutions showed a 300 per cent increase in speed to flag compromised merchants, while Visa has achieved an 85 per cent reduction in false positives. Similarly, the Commonwealth Bank of Australia leveraged AI to reduce scam losses by 50 per cent and fraud cases by 30 per cent.

Critical insights

With AI, institutions can also take real-time action by blocking suspicious transactions before they’re completed, flagging accounts for immediate review, or alerting customers during suspicious activity. For financial institutions, AI-driven fraud tools can offer critical insight into how fraud is evolving, and thus stay ahead of the curve effectively. This leads to two things: teams can strengthen security proactively, thus refining customer experiences and protecting long-term trust.

However, this is just the beginning. AI-powered analytics also offer deeper insights into customer behaviour and risk. Once AI has a clearer view of customer profiles, financial institutions can fine-tune their offerings and personalise communication, based on each customer. In fact, generative AI is already being used to analyse individual user data to make marketing smarter and more tailored, as we speak.

AI and ML are also transforming how creditworthiness is assessed in lending. This works by pulling in alternative data, like utility payments or cash flow histories. This alternative data now provides a fuller picture of a borrower and their credit history, especially useful for those who do not have formal credit histories. Thus, this works by opening up avenues to responsibly lend to undeserved populations and widens the inclusivity of financial services in rural areas.

Integrates easily

Today’s best AI platforms are designed to scale and plug into diverse financial ecosystems. Thanks to application programming interfaces (APIs), they integrate easily across banks, non-banking financial companies (NBFCs), insurers, and fintechs. Such APIs can be tailored to regional needs, specific products, and varying levels of risk appetite.

However, as we push the boundaries of what AI can achieve, it is paramount that we engage in responsible behaviour while doing so. We need to ensure that there is a fine balance between innovation and regulation. While this might prove challenging at times, it is essential to uphold trust and ensure sustainable growth in this sector. Regulatory frameworks are now being developed around the use of AI. The European Union (EU) has been the first to do so, with their latest EU AI Act. While India does not have any such Act yet, the Reserve Bank of India has also recently established an eight-member committee to develop a Framework for Responsible and Ethical AI (FREE-AI) for adoption in the financial sector. In addition, we can use some of our existing legal frameworks, such as the Information Technology Act, 2000, and the Digital Personal Data Protection Act, 2023, to ensure that we use AI responsibly.

Beyond the technological defences that AI can provide to counter fraud, collaboration across the ecosystem is absolutely essential as well. Fraud mitigation is a shared responsibility, and no singular entity or organisation should have to bear the brunt of tackling fraud on its own. Thus, all participants of the ecosystem, including financial institutions, technology providers, and even consumers, need to unite to combat this common threat. Our goal is not just to react to threats, but to proactively build a more secure and resilient digital ecosystem for everyone.

Financial crime is getting more sophisticated by the day. But so is our ability to fight it. With AI, the financial industry is no longer stuck playing defence. It’s moving towards a smarter, faster, and more resilient future. A future where fraud detection is not just better, but built for what’s next.

The writer is Co-Founder and CEO, Finarkein

Published on August 28, 2025