AI Is the New Watchdog: Fraud Prevention in the Modern Era

Fraud is no longer an exception—it has become widespread. The Ministry of Home Affairs estimates that in the first five months of 2025, Indians lost ₹7,000 crore to online fraud. A 2025 report from the Association of Financial Professionals found that 79% of US organizations experienced fraud attacks or attempts in 2024.

Legacy Fraud Prevention Systems Are Ineffective

Banks and credit card companies often reimburse fraud victims, incurring heavy losses in the process. As a countermeasure, they invest significant resources in detecting and preventing fraud before it occurs. Traditionally, financial institutions have relied on manual reviews and simple rules-based systems.

However, these systems are resource-intensive and cannot scale. Rules-based models employ static, predefined logic, leaving them unable to keep pace with evolving fraud tactics.

How AI Can Do Better

AI models learn from historical data and continuously adapt to new threats in real time, making them far more effective at identifying subtle anomalies and evolving fraud patterns. They can analyze vast amounts of data instantly and accurately, reducing the need for manual reviews while minimizing false positives.

Early Adopters in India

Indian banks have been early adopters of AI for fraud prevention. One early success story is the video-based onboarding system at a major Indian bank. The system allows customers to open accounts without visiting a branch.

When a new customer wants to open an account, the bank’s app asks for their name, Aadhaar number, and PAN number. The customer is then directed to join a video session. An AI-powered facial recognition algorithm compares the client’s video to the official picture on file with the government and verifies their identity. If the system detects a mismatch, the process is blocked. Since the video captures the client’s face from several angles, the recognition algorithm is more accurate than methods that rely only on uploaded photos or documents.

In 2024, the Reserve Bank of India (RBI) launched Mulehunter.ai, an AI system that detects mule accounts—accounts used by criminals to launder illicit funds by recruiting individuals to transfer money on their behalf. Mulehunter.ai scans vast amounts of transaction data, flagging potential mule accounts by using machine learning algorithms trained on historical fraud data. It employs network analysis to trace links between accounts and data mining to detect deviations from normal usage. This allows the system to flag suspicious activity, such as sudden high-value transfers or repeated rapid cross-border transactions.

AI-Based Fraud Prevention Systems Deployed Internationally

Credit card companies have been among the first to adopt AI-driven fraud prevention systems. One leading American provider, for instance, uses AI in its risk management platform to make more than 8 billion decisions annually, ranging from identifying fraudulent account openings to setting credit limits.

Most of these decisions are what AI developers call “dependent events,” meaning each decision cannot be viewed in isolation. For example, consider three transactions by a customer: buying a coffee near their home, an iPhone across the city, and a $500 bookstore gift card. Individually, none of these transactions looks suspicious. But when considered together, an effective AI system should detect the geographic inconsistency and rapid spending and flag the transaction.

To identify fraud in such cases, developers use Recurrent Neural Network (RNN) models, designed to recognize patterns in sequences. Unlike traditional models that treat inputs independently, RNNs use internal memory to “remember” previous inputs, making them ideal for analyzing transaction histories and detecting fraud across multiple transactions.

Watch This Space

Fraud is no longer a one-off risk—it is constant and adaptive. Traditional systems remain rigid, reactive, and costly, while AI-driven models are proving more agile, scalable, and accurate. Yet what we are seeing today is only the beginning.

Banks, fintechs, and payment networks are already ramping up investment in advanced AI tools. One leading American credit card provider is testing generative AI to scan billions of transactions, with the promise of spotting compromised cards twice as fast.

Meanwhile, researchers are building adaptive models that retrain themselves on fresh data, boosting their ability to detect scams that morph overnight. Behavioral biometrics, such as typing patterns or device movements, are also emerging as silent markers of fraud, adding another layer of defense without disrupting the user experience.

The direction is clear: fraudsters will keep innovating, but so will the defenders. The real battle is no longer between man and machine—it is between machines on both sides, and the winners will be the ones that learn fastest.