Adaptive Credit Scoring With Analytic Hierarchy Process

Credit risk assessment for consumers has been a cornerstone of risk management in financial institutions 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 applications by prospective borrowers. With the evolution of the credit risk management practices, more quantitative methods such as credit scorecards have been developed, which is implemented through the use of logistic regression, decision trees and neural networks. However, such approaches proved to be inadequate with the validity and effectiveness of the approaches doubted 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-reliance on mathematical modeling resulting in a divorce between reality and model. To remedy the problem of over-reliance on pure quantitative models, Clark Abrahams and Mingyuan Zhang introduced the Comprehensive Credit Assessment Framework (CCAF) that attempts to address weaknesses in the existing credit risk assessment system, and provides flexibility with better accuracy, transparency and simplicity for the various stakeholders in the credit lending business. Unlike the 5 ‘C’s of credit assessment which provides very coarse segregation of potential borrower and focus more on past performance which can be a reflection of better credit environment and no longer represent the current environment, CCAF caters for a more fine-grain segmentation that allow for specific action for specific groups of borrower which results in an adaptive framework that takes in new inputs to improve its predictiveness. This paper proposes to implement the CCAF by utilizing Analytic Hierarchy Process (AHP) for consumer credit segment to establish a new hybrid version of the framework – AHP-CCAF model. The new model is first tested on 3 classical 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 model is feasible and has better forecasting capability than the traditional methods. ∗ Corresponding author Proceedings of the International Symposium on the Analytic Hierarchy Process 2013