Identifying suspicious activity is a complex task. Transaction monitoring models that include customer segmentation offer more holistic analytics.
Holistic, risk-based analytics is necessary for any bank or financial services company seeking to monitor suspicious activity and mitigate risk. Integrating customer segmentation into transaction monitoring models enhances an organization’s approach and overall risk posture.
As banks and financial services companies grow in asset size and customer base, suspicious activity monitoring becomes increasingly more complex. While transaction monitoring models can help banks and financial services companies mitigate certain risks associated with money laundering, it is essential that these models are used properly. Customer segmentation is one way to improve the efficiency of transaction monitoring models. This article discusses two segmentation approaches, how these approaches can be integrated, the importance of orthogonality, and common misconceptions about transaction monitoring segmentation.
Customer segmentation using risk-based data insights elevates the effectiveness and efficiency of transaction monitoring models. It allows companies to better cluster and set more precise thresholds for monitoring groups of similarly behaving customers.
Traditionally, segmentation was qualitative. It was used to delineate the lines of business by clustering customers based on common attributes and characteristics. Adding a quantitative dimension increases the impact of segmentation by reducing noise alerts associated with transaction monitoring scenarios.
Banks and financial services companies must have processes in place to gain a reasonable understanding of their customers along with access to accurate and consistent data. More specifically, banks and financial services companies must be able to ingest and analyze historical customer behavior to establish future “expected” behavior and create the best segmentation model possible. Segmentation can be performed in several ways, and effectively combining these ways can result in a much more complete model.