March 20, 2025
Financial crime and fraud are growing at a faster clip than detection and prevention systems can adapt. That has been the case because traditional systems and processes are primarily reactive and hence slow; worse, they are quite expensive as well.
Any failure results in business loss as well as regulatory non-compliance, inviting penalties from regulators. An Enzuzo studied complied bank fine to show that they have indeed incurred over $10 billion in fines related to anti-money laundering (AML) compliance failures. Additionally, cumulative penalties for AML violations have exceeded $55 billion since 2008, according to Fenergo.
Traditional Fraud models fail due to inherent limitations:
- Rigid and inflexible: Predefined rules and historical data are the cornerstones for these modes, making them unable to adapt to new and evolving fraud tactics.
- Manual Analysis: Time-consuming and error-prone manual methods are largely used to analyze customer records and internal controls.
- Lacks Real-time Detection: More often than not, traditional models are not equipped to analyze and hence detect fraud in real-time.
The flavour of the season, AI/ML, can provide a leg-up to BFSI to overcome the limitations of traditional models, but one shouldn’t mistake it for a ‘silver bullet’.
Modern fraud detection techniques leverage advanced technologies to keep pace with Financial Crime and Fraud:
- AI/ML: ML algos can analyze vast amounts of data to identify patterns and anomalies that may indicate fraudulent activity. AI models can continuously learn and adapt to new fraud tactics, improving their accuracy over time.
- Behavioural Analytics: Model to detect behaviour deviations from standard patterns and flag them off. Further, these models need to be trained on industry-specific data to be more effective.
- Real-Time Transaction Monitoring: In today’s world, where every second millions and billions of transactions are being processed, BFSI need real-time monitoring systems that can detect and respond to suspicious transactions on the fly.
These modern techniques can offer significant improvements in fraud detection by providing real-time detection, adaptability, and the ability to analyze large datasets to identify intricate fraud patterns. We at Cedar-IBSi Capital believe that this is a significant and evergreen opportunity for new-age banking technology start-ups/ founders. If you are building in this space, do reach out to us.
