Large-scale Data-driven Segmentation of Banking Customers

This paper presents a novel big data analytics framework for creating explainable personas for retail and business banking customers. These personas are essential to better tailor financial products and improve customer retention. This framework is comprised of several components including anomaly detection, binning and aggregation of contextual data, clustering of transaction time series, and mining association rules that map contextual data to cluster identifiers. Leveraging rich transaction and contextual data available from nearly 60,000 retail and 90,000 business customers of a financial institution, we empirically evaluate this framework and describe how the identified association rules can be used to explain and refine existing customer classes, and identify new customer classes and various data quality issues. We also analyze the performance of the proposed framework and show that it can easily scale to millions of banking customers.