New applications in financial crime analytics

Financial crime is a fast-growing problem for financial services firms in Asia. With the scale and sophistication of criminal activity on the rise, and regulatory scrutiny and penalties increasing, firms are under pressure to tighten compliance procedures and improve the effectiveness and efficiency of their efforts to tackle money laundering and fraud.

Dealing with outmoded systems and patched-up processes in detecting, monitoring and eliminating potential threats, firms are spending millions channelling additional resource to this activity.

As a result, demand is growing for more sophisticated solutions to support the investigation process. Recent developments in robotics process automation (RPA) and machine learning promise to revolutionise existing practices, with analytics-driven case management, automated evidence gathering and entity risk visualisation freeing up time for more strategic tasks.

Key topics discussed include:

• Insights from a recent Risk.net survey of current practices
• The changing shape and scale of the financial crime threat
• Key challenges in financial crime investigation
• How firms can achieve the greatest efficiency gains
• The latest trends in the application of machine learning and RPA
• Specific use-cases in the area of anti-money laundering and fraud.

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