Introduction

Transaction Monitoring (TM) is a critical process for financial institutions (FIs) to protect themselves against financial, regulatory, and reputational risks associated with surveilling customer activities for potentially suspicious activities.  TM is the monitoring of customer transactions after their execution using algorithms and rules to identify potentially illicit activities requiring additional review, investigation and reporting, where necessary.  TM is heavily dependent on accurate data, including a customer’s historical and expected activity, beneficial ownership information, and updated customer information. Given the critical role that accurate data plays in creating a risk-based, quantifiable risk assessment framework, Financial Institutions (FIs) are prioritizing efforts to strengthen their data management solutions.  

Drivers of Speed and Accuracy in Transaction Monitoring

In response to the evolving nature of financial crimes, the industry is increasingly seeking innovative, efficient, and trustworthy data solutions. Transactional data needs to be managed effectively to support FIs in their mission to control fraud and support the global regulators’ needs for evidence of effective monitoring. For example:                      

• Rule definition and pre-implementation testing: TM detection rules and scenarios are designed to identify suspicious patterns for exception handling. They use risk-based customer and product segmentation, employ statistical tools to finetune calibrations, and perform pre-implementation testing of TM rules to measure compatibility.   
• Alert scoring and prioritization: Risk-weighted scores prioritize higher risk alerts for expedited review, ensuring continual effectiveness assessments regarding the relevancy of detection scenarios, assumptions and calibration of rule threshold values and parameters. 
• Timely suspicious activity reporting: FIs are responsible for timely reporting of suspicious activity and demonstrating that their TM solution is effective at identifying potentially fraudulent activity.  Accurate and timely processing demands high quality data. 
• Data quality benchmarking: Periodic testing of the integrity, accuracy, and quality of data can help identify any weaknesses and provide evidence of an adaptive, relevant TM solution.   

How Can  FIs Strengthen their TM Platforms?

TM is a complex challenge to solve, and should FIs consider a range of complementary capabilities to strengthen the solution.  By focusing on specific needs, customized offerings can be prioritized and developed in a modular, flexible architecture. For example:

In Conclusion