Scale at Speed™
Nextgen AML Integrating AI with Traditional Methods for Smarter Financial Crime Detection


Abstract
The landscape of Anti-Money Laundering and Counter-Terrorism Financing (AML/CTF) is rapidly changing. Traditional rule-based systems face challenges with high false-positive rates and the sophistication of modern financial crimes. With trillions of dollars laundered globally, financial institutions are increasingly turning to AI-powered solutions.
This whitepaper explores various AML/CDD/KYC/KYT techniques and the crucial elements for effective implementation, including people, processes, and technology. It delves into Discai's platform, highlighting its hybrid approach that combines AI and rule-based methods for enhanced AML effectiveness. The paper also addresses the importance of enterprise-wide monitoring, network analysis, and anomaly detection. Finally, it outlines the strategic partnership between Discai and Tech Mahindra and the combined value they offer clients through their joint go-to-market strategy.
Key Takeaways
- Traditional rule-based AML systems are reaching their limits.
The whitepaper outlines how a hybrid approach—combining AI with existing rule engines—enhances accuracy and operational efficiency. - Transaction-level monitoring (KYT) is critical to effective financial crime detection.
Learn how leading institutions are leveraging AI models to detect anomalies in real time and reduce missed risks. - High false positive rates significantly impact cost and resource allocation.
The paper details how advanced model architectures can optimize investigation workloads without compromising regulatory standards. - Explainability and observability are essential for regulatory alignment.
The whitepaper explores mechanisms that ensure AI adoption is transparent, auditable, and aligned with compliance expectations. - The solution is already operational at scale.
Built within KBC and delivered globally through Tech Mahindra, the approach offers proven outcomes, not lofty promises.