An optimised credit scorecard to enhance cut-off score determination

Nico Kritzinger,Gary W Van Vuuren

doi:10.4102/sajems.v21i1.1571

Nico Kritzinger, Gary W Van Vuuren

Open Access

https://doi.org/10.4102/sajems.v21i1.1571

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Abstract

Background: Credit scoring is a statistical tool allowing banks to distinguish between good and bad clients. However, literature in the world of credit scoring is limited. In this article parametric and non-parametric statistical techniques that are used in credit scoring are reviewed. Aim: To build an optimal credit scoring matrix model to predict which clients will go bad in the future. This article also illustrates the use of the credit scoring matrix model to determine an appropriate cut-off score on a more granular level. Setting: Data used in this article are based on a bank in South Africa and are Retail Banking specific. Methods: The methods used in this article were regression, statistical analysis, matrix and comparative study. Results: The matrix provides uplift in the Gini-coefficient when compared to a one-dimensional model and provides greater granularity when setting the appropriate cut-off. Conclusion: The article provides steps to construct a credit scoring matrix model to optimise separation between good and bad clients. An added contribution of the article is the manner in which the credit scoring matrix model provides a greater granularity option for establishing the cut-off score for accepting clients, more appropriately than a one-dimensional scorecard.

Highlights

One of the most important elements driving a bank’s existence and continuance is the ability to grant credit to the appropriate client who is less likely to go bad
The Credit Scoring Matrix Model (CSMM) build consisted of two components, namely the internal application scorecard and the credit bureau score
In the last 25 years credit scoring has grown, in the banking industry, making the separation between good and bad clients more critical to have effective credit risk management and to reduce future bad debts, which emphasises the significance of this research

Summary

Introduction

One of the most important elements driving a bank’s existence and continuance is the ability to grant credit to the appropriate client who is less likely to go bad. Various modelling techniques exist to model credit scoring with most of them being statistical; for example, linear regression, discriminant analysis, probit analysis, logistic regression, decision trees, expert systems, neural networks and genetic programming (Abdou & Pointon 2011:66–68). Given this range of statistical techniques, no optimal technique for scorecard construction exists (Abdou & Pointon 2011:68). In this article parametric and non-parametric statistical techniques that are used in credit scoring are reviewed

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Results

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