Abstract
Credit scoring is an important tool used by financial institutions to correctly identify defaulters and non-defaulters. Support Vector Machines (SVM) and Random Forest (RF) are the Artificial Intelligence techniques that have been attracting interest due to their flexibility to account for various data patterns. Both are black-box models which are sensitive to hyperparameter settings. Feature selection can be performed on SVM to enable explanation with the reduced features, whereas feature importance computed by RF can be used for model explanation. The benefits of accuracy and interpretation allow for significant improvement in the area of credit risk and credit scoring. This paper proposes the use of Harmony Search (HS), to form a hybrid HS-SVM to perform feature selection and hyperparameter tuning simultaneously, and a hybrid HS-RF to tune the hyperparameters. A Modified HS (MHS) is also proposed with the main objective to achieve comparable results as the standard HS with a shorter computational time. MHS consists of four main modifications in the standard HS: (i) Elitism selection during memory consideration instead of random selection, (ii) dynamic exploration and exploitation operators in place of the original static operators, (iii) a self-adjusted bandwidth operator, and (iv) inclusion of additional termination criteria to reach faster convergence. Along with parallel computing, MHS effectively reduces the computational time of the proposed hybrid models. The proposed hybrid models are compared with standard statistical models across three different datasets commonly used in credit scoring studies. The computational results show that MHS-RF is most robust in terms of model performance, model explainability and computational time.
Highlights
Credit risk evaluation is a crucial routine of risk management in financial institutions.Credit scoring models are the main tool utilized to make credit granting decisions where the probability of default resembles the entropy concept, i.e., probabilistic measure of uncertainty
The datasets used in the experiments are the German and Australian datasets which are publicly available at the UCI repository
This section reports the experimental results obtained from the different credit scoring models across the three credit datasets based on model performances, model explainability and computational time
Summary
Credit risk evaluation is a crucial routine of risk management in financial institutions. Credit scoring models are the main tool utilized to make credit granting decisions where the probability of default resembles the entropy concept, i.e., probabilistic measure of uncertainty. To better measure risk, more accurate classification models are needed. Though statistical models are usually the preferred option, Artificial Intelligence (AI) models are beginning to be favoured for their accuracy and flexibility in the face of the volume of data. Advances in these techniques have further increased their popularity in risk assessments. Support Vector Machines (SVM) and Random Forest
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