Transaction monitoring plays a critical role in detecting financial crime, yet rule-based models face challenges such as complexity and limited adaptability, prompting exploration of advanced modeling techniques.

Machine learning offers flexibility and adaptability in transaction monitoring, presenting advantages over rule-based models by learning patterns, handling dynamic scenarios, and uncovering hidden correlations.

Strategies like Automated Alert Prioritization and logistic regression model offer potential solutions to enhance transaction monitoring effectiveness, though human oversight remains crucial for mitigating errors and ensuring model explainability.

Effective model documentation and maintenance practices, including clear templates, visualizations, and control mechanisms, are essential for advancing model transparency and understanding in transaction monitoring processes.