Development of credit scoring models is important for financial institutions to identify defaulters and nondefaulters when making credit granting decisions. In recent years, artificial intelligence (AI) techniques have shown successful performance in credit scoring. Support Vector Machines and metaheuristic approaches have constantly received attention from researchers in establishing new credit models. In this paper, two AI techniques are reviewed with detailed discussions on credit scoring models built from both methods since 1997 to 2018. The main discussions are based on two main aspects which are model type with issues addressed and assessment procedures. Then, together with the compilation of past experiments results on common datasets, hybrid modelling is the state-of-the-art approach for both methods. Some possible research gaps for future research are identified.
Credit scoring is an important tool used by financial institutions to correctly identifydefaulters and non-defaulters. Support Vector Machines (SVM) and Random Forest (RF) are theArtificial Intelligence techniques that have been attracting interest due to their flexibility to accountfor 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 benefitsof accuracy and interpretation allow for significant improvement in the area of credit risk andcredit scoring. This paper proposes the use of Harmony Search (HS), to form a hybrid HS-SVM toperform feature selection and hyperparameter tuning simultaneously, and a hybrid HS-RF to tunethe hyperparameters. A Modified HS (MHS) is also proposed with the main objective to achievecomparable results as the standard HS with a shorter computational time. MHS consists of fourmain modifications in the standard HS: (i) Elitism selection during memory consideration insteadof random selection, (ii) dynamic exploration and exploitation operators in place of the originalstatic operators, (iii) a self-adjusted bandwidth operator, and (iv) inclusion of additional terminationcriteria to reach faster convergence. Along with parallel computing, MHS effectively reduces thecomputational time of the proposed hybrid models. The proposed hybrid models are comparedwith standard statistical models across three different datasets commonly used in credit scoringstudies. The computational results show that MHS-RF is most robust in terms of model performance,model explainability and computational time.
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