Challenge

In the highly regulated banking industry, performing enhanced due diligence on customers is not just a requirement—it’s a legal obligation. Anti-money laundering (AML) analysts are tasked with generating comprehensive reports on customers flagged for suspicious transactions. Given the massive volumes of data originating from disparate systems, compiling these reports for hundreds of thousands of customers is both time-consuming and resource-intensive.

Our Approach

Leveraging the power of machine learning and generative AI, we engineered an advanced solution that automates the creation of due diligence reports. This AI-driven system meticulously analyses transactional data to identify anomalies that deviate from established customer behavior patterns. To further enhance the reports, we employed graph theory to visualise relationships between related parties and map out the flow of funds. Additionally, adverse media data is seamlessly integrated, providing a richer contextual backdrop for each report.

Our solution consolidates multiple data sources into a unified platform, offering a comprehensive view that significantly reduces the manual burden on AML analysts. By automating these routine tasks we empower analysts to focus on higher-value activities for the business.

Outcomes

• Significant Time Savings: Reduced the time required to generate a consolidated customer due diligence report from up to 6 hours to under 20 seconds.

• Operational Scalability: Enabled a tenfold increase in the rate of due diligence processing with the same number of AML analysts.

• Enhanced Analysis: Integrated diverse data sources, providing AML analysts with richer, more comprehensive insights into suspicious transactions.

• Empowered Workforce: Freed AML analysts from routine tasks, allowing them to focus on higher-value analytical work that complements AI-driven insights.