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Credit Risk Scoring Models

Published: July, 2010
Encyclopaedia of Quantitative Finance, Wiley and Sons
By Gabriele Sabato
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Abstract

Credit scoring models play a fundamental role in the risk management practice at most banks. They are used to quantify credit risk at counterparty or transaction level in the different phases of the credit cycle (e.g. application, behavioural, collection models). The credit score empowers users to make quick decisions or even to automate decisions and this is extremely desirable when banks are dealing with large volumes of clients and relatively small margin of profits at individual transaction level (i.e. consumer lending, but increasingly also small business lending). In this article, we analyze the history and new developments related to credit scoring models. We find that with the new Basel Capital Accord, credit scoring models have been remotivated and given unprecedented significance. Banks, in particular, and most financial institutions worldwide, have either recently developed or modified existing internal credit risk models to conform with the new rules and best practices recently updated in the market. Moreover, we analyze the key steps of the credit scoring model’s lifecycle (i.e. assessment, implementation, validation) highlighting the main requirement imposed by Basel II. We conclude that banks that are going to implement the most advanced approach to calculate their capital requirements under Basel II will need to increase their attention and consideration of credit scoring models in the next future.

Introduction

Credit scoring models play a fundamental role in the risk management practice at most banks. Commercial banks’ primary business activity is related to extending credit to borrowers, generating loans and credit assets. A significant component of a bank’s risk, therefore, lies in the quality of its assets that needs to be in line with the bank’s risk appetite . In order to manage risk efficiently, quantifying it with the most appropriate and advanced tools is an extremely important factor in determining the bank’s success.

Credit risk models are used to quantify credit risk at counterparty or transaction level and they differ significantly by the nature of the counterparty (e.g. corporate, small business, private individual). Rating models have a long term view (Through The Cycle) and have been always associated with corporate clients, financial institutions and public sector. Scoring models, instead, focus more on the short term (Point In Time) and have been mainly applied to private individuals and, more recently, extended to small and medium sized enterprises (SMEs). In this article, we will focus on credit scoring models giving an overview of their assessment, implementation and usage.

Since the 1960s, larger organizations have been utilizing credit scoring to quickly and accurately assess the risk level of their prospects, applicants and existing customers mainly in the consumer lending business. Increasingly, midsize and smaller organizations are appreciating the benefits of credit scoring as well. The credit score is reflected in a number or letter(s) that summarizes the overall risk utilizing available information on the customer. Credit scoring models predict the probability that an applicant or existing borrower will default or become delinquent over a fixed time horizon. The credit score empowers users to make quick decisions or even to automate decisions and this is extremely desirable when banks are dealing with large volumes of clients and relatively small margin of profits at individual transaction level.

Credit scoring models can be classified into three main categories: i.e. application, behavioural and collection models, depending on the stage of the consumer credit cycle in which they are used. The main difference between them lies in the set of variables that are available to estimate the client’s creditworthiness, i.e. the earlier the stage in the credit cycle the lower the number of specific client information available to the bank. This generally means that application models have a lower prediction power than behavioural and collection models.

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