Machine learning methods application for consumer banking

Abstract

Machine learning (ML) methods are effective tools for analysis of many actual problems in modern banking. Increasing growth of data and rapid digitalization underpin the acceleration of ML implementation. These processes are especially noticeable in consumer banking because banks have millions of the retail customers. The first goal of our research is to form an extended review ML application in consumer banking. From one side we have identified the most developed ML methods, which are applied in this segment (for example different types of regressions, fuzzy clustering, neural network, principal component analysis etc.). From the other side, we point out two multi-purpose tools used by banks in consumer segment intensively, namely scoring and clustering. Secondly, our goal is to present some innovative applications of ML methods to the analysis of each task. This includes several applications for scoring models and fuzzy clustering application. All applications are oriented to make banks business processes more effective. Considered applications were realised on real data from the Ukrainian banking industry.