Introduction
The frequency and complexity of financial crimes is increasing
In recent decades, the evolution of fraud and financial crime detection systems was spurred by new risk trends and technology advancements. While fraud detection traditionally used artificial intelligence (AI) and machine learning techniques, financial crimes utilized complex rules engines to detect cases. Over the last several years, financial services firms have extended the capabilities of AI and machine learning to financial crimes alert management with significant results, such as reductions in false positives, improved risk detection, and increased automation at scale.
However, though fraud and financial crimes functions use similar monitoring techniques, they still largely operate independently within most financial firms. This model may have been appropriate years ago when fraud and financial crime schemes were dissimilar and managed accordingly, but current factors like channels, payment rails, and decentralization are blurring the line between fraud and financial crimes.
