Britam Innovators Develop AI Tool to Combat Money Laundering

In a highly interconnected world, financial institutions face growing challenges to combat money laundering and enhance transparency.

Britam’s #BlueTribe Hackathon sparked fresh, bold solutions from employees to address these critical industry challenges.

Among the top three winners was a team led by RuthClaire Mugambi (Senior Compliance Officer) who developed the Risk-Based Due Diligence Optimization Tool (RBDOT)—an AI-powered tool to enhance risk management and due diligence

Other members of the RBDOT Team include Ezra Mokua (Assistant Compliance Manager) and Lameck Agure (Software Developer).

RBDOT addresses discrepancies in due diligence and suspicious transactions, both significant vulnerabilities associated with money laundering within the financial services industry.

The team discussed their innovation:

1. What does RBDOT do?

RBDOT focuses on risk profiling and transaction monitoring to identify and mitigate potential risks in financial transactions.

2.     How does RBDOT identify and prioritize high-risk areas?

RBDOT utilizes machine learning algorithms to analyze data, detect patterns, and identify anomalies. This helps prioritize areas that exhibit unusual or suspicious behavior, ensuring that high-risk areas are addressed promptly.

3.     What technologies does RBDOT leverage to enhance due diligence and transaction monitoring?

RBDOT leverages AI and machine learning technologies to enhance the accuracy and efficiency of due diligence processes and transaction monitoring.

4.    Can you provide an example of how RBDOT will improve accuracy and efficiency in risk management?

Currently, the task is performed manually using MS Excel sheets, requiring at least 8 hours per day and conducted twice a week. This amounts to 16 hours per week or 64 hours per month dedicated solely to this task. With the introduction of the new automated tool, the same task will take only 1 hour per day, reducing the workload to just 5 hours per week or 20 hours per month. Overall, automation will save 44 hours per month, resulting in a 69% improvement in time efficiency.

5.      Can any other industries benefit from using RBDOT apart from insurance?

Yes, sectors such as banking can benefit from RBDOT’s capabilities in risk management and transaction monitoring.

6.    How is the RBDOT supposed to integrate with existing risk management systems?

RBDOT integrates seamlessly with existing systems through APIs and data warehouses, allowing for smooth data exchange and interoperability with current risk management frameworks.

7.     How will the RBDOT stay updated with evolving regulatory requirements and risk landscapes?

RBDOT continuously updates its models and algorithms through machine learning, ensuring it adapts to new regulatory requirements and emerging risk trends.

8.    How will the RBDOT handle false positives and ensure the accuracy of its risk assessments?

RBDOT employs a robust review process where false positives are flagged and reviewed by experts. This iterative process helps in refining the system and improving the accuracy of risk assessments.

9.    How will the RBDOT compare to other risk management tools in the market?

RBDOT stands out by leveraging the latest technologies such as AI and machine learning, providing more accurate, efficient, and adaptive risk management solutions compared to traditional tools.

10.  How will RBDOT ensure compliance with data privacy and protection regulations?

RBDOT ensures compliance by embedding IT safety protocols, implementing a user access matrix, and enforcing strong password policies to protect sensitive data and maintain privacy standards.

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