Financial Event Processing System for Suspicious Activity Detection
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Solution Overview
Problem
Financial institutions face significant challenges in detecting and monitoring illicit activities within their systems due to the complexity of transactions and the need to comply with governmental regulations, which can result in substantial monetary and reputational burdens, as well as the difficulty in documenting investigations and justifying scrutiny levels for customers.
Innovation Solution
A financial crimes event processing system that ingests data from various sources, enriches event information, groups events based on common factors, and scores them using pre-defined risk factors and customer experience data to automatically determine if a case for investigation should be created, with the option to statistically sample events below a certain threshold for further analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If every transaction is monitored for illicit activity, then detection reliability is improved, but processing time and system complexity increase significantly
Solution Approach 1:
The patent segments transactions into different risk categories using a risk-based monitoring approach. High-risk transactions are subjected to detailed scrutiny while low-risk transactions are processed more efficiently, allowing the system to maintain high detection reliability for suspicious activities while reducing overall processing time through differentiated handling.
Solution Approach 2:
The system dynamically adjusts monitoring parameters and thresholds based on transaction characteristics, customer risk profiles, and emerging threat patterns. This allows the system to optimize detection sensitivity and processing efficiency by changing monitoring intensity parameters according to specific transaction contexts.
2Reliability
If comprehensive monitoring is implemented, then detection capability is improved, but operational burden and costs increase
Solution Approach 1:
The system implements automated risk assessment and case management capabilities that reduce manual operational burden. Algorithms automatically evaluate transactions, assign risk scores, and prioritize cases for investigator review, allowing the system to serve itself in preliminary assessment tasks while maintaining comprehensive detection capability.
Solution Approach 2:
Manual monitoring processes are replaced with automated computer-based systems that use algorithms, machine learning models, and data analytics to perform risk assessment. This substitution reduces operational burden while enhancing detection capability through consistent, scalable automated analysis.
3Reliability
If detailed documentation of investigations is maintained, then compliance reliability is improved, but processing efficiency decreases
Solution Approach 1:
The system performs preliminary documentation and data collection during the investigation process itself, rather than requiring separate documentation steps later. Investigation activities, decisions, and rationales are automatically captured and stored as cases progress, ensuring compliance documentation is maintained without reducing processing efficiency.
Solution Approach 2:
The system provides automated feedback loops that track investigation progress, document decisions, and update case status in real-time. This feedback mechanism ensures compliance documentation is continuously maintained and updated without requiring separate manual intervention, preserving both compliance reliability and processing efficiency.
Data Source
AI summary
Event processing for suspicious activity detection is disclosed. Financial events can be detected by receiving data from a plurality of detection channels. The data can optionally be enriched by adding additional details about events. An event or group of events is scored based on pre-defined risk factors and experience, and the resulting score can be compared to a pre-defined threshold. A case for investigation is established when the score is above the pre-defined threshold. Events for which the score is below the pre-defined threshold are sampled to provide the experience for the scoring. Events can be scored by obtaining a plurality of predictors, collecting training data regarding existing cases, and running a logistic regression to obtain weights for the predictors. The likelihood of a case for investigation resulting in a suspicious activity report can then be determined.