The rapid ascent of the FinTech industry—with its emphasis on speed, convenience, and accessibility—has brought about a parallel rise in the sophistication and volume of financial fraud. Traditional rule-based fraud detection systems, which rely on a set of static, predefined rules, are increasingly unable to keep pace with the dynamic and evolving tactics of fraudsters. In this high-stakes environment, artificial intelligence (AI) and machine learning (ML) have emerged as the new front-line defense, revolutionizing the way FinTech companies and financial institutions protect themselves and their customers.

The Limitations of Traditional Systems

Traditional fraud detection typically operates on a simple principle: if a transaction meets a set of pre-programmed rules (e.g., “flag any transaction over $5,000” or “block any transaction originating from an unusual country”), it is flagged for review. While this approach can be effective for simple, well-known fraud patterns, it has significant drawbacks:

• High False Positives: Rigid rules often