Data science methods for response, incremental response and rate sensitivity to response modelling in banking

Abstract

This work provides a review of data science methods that can be used to address a wide variety of business problems in the banking sector. The paper examines three modelling paradigms: the response, incremental response and the rate sensitivity to response approaches, emphasising the role they play to address these problems. These paradigms and the methods they involve are presented in combination with real cases to illustrate their potential in extracting valuable business insights from data. It is enhanced their usefulness to help business experts like risk managers, commercial managers, financial directors and chief executive officers to plan their strategies and guide decision making on the basis of the insights given by their outcomes. The scope of the work is twofold: it presents a unified view of the methods and how the fit the aforementioned paradigms while, at the same time, it examines some business cases for their application. Both issues will be of interest for technical and managerial teams involved in running data science projects in banking.