Adaptive Credit Scoring with Analytic Hierarchy Process

Abstract

Credit risk assessment for consumers has been a cor nerstone of risk management in financial institutio ns and constitutes a component of the three pillars of Basel II. Traditionally, the concept of 5 ‘C’s was widely adopted by financial institutions as the key basis for credit risk assessment for loan applicat ions by prospective borrowers. With the evolution of the c redit risk management practices, more quantitative methods such as credit scorecards have been develop ed, which is implemented through the use of logisti c regression, decision trees and neural networks. How ever, such approaches proved to be inadequate with the validity and effectiveness of the approaches do ubted especially in the light of the 2008 sub-prime financial crisis in US, which was partly triggered by poor quantitative modeling as well as over-relia nce on mathematical modeling resulting in a divorce bet ween reality and model. To remedy the problem of over-reliance on pure quantitative models, Clark Ab rahams and Mingyuan Zhang introduced the Comprehensive Credit Assessment Framework (CCAF) that attempts to address weaknesses in the existing credit risk assessment system, and provide s flexibility with better accuracy, transparency an d simplicity for the various stakeholders in the cred it lending business. Unlike the 5 ‘C’s of credit assessment which provides very coarse segregation o f potential borrower and focus more on past performance which can be a reflection of better cre dit environment and no longer represent the current environment, CCAF caters for a more fine-grain segmentation that allow for specific action for specifi c groups of borrower which results in an adaptive fra mework that takes in new inputs to improve its predictiveness. This paper proposes to implement th e CCAF by utilizing Analytic Hierarchy Process (AHP) for consumer credit segment to establish a ne w hybrid version of the framework ‐ AHP-CCAF model. The new model is first tested on 3 classica l credit risk data to illustrate the feasibility of the model. The paper compares the performance of the new model against traditional method of Decision Tree Analysis. Results shows that the proposed mod el is feasible and has better forecasting capabilit y than the traditional methods.