Application of Artificial Intelligence Techniques for Credit Risk Evaluation

Abstract

Credit risk is the most challenging risk to which financial institution are exposed. Credit scoring is the main analytical technique for credit risk evaluation. Application of artificial intelligence has lead to better performance of credit scoring models. In this paper a hybrid model for credit scoring is designed which applies ensemble learning for credit granting decisions. Ten classifier agents are utilized as the members of ensemble model. Support vector machine, Neural Networks and Decision Tree as base classifiers were compared based on their accuracy in classification. Since even a small improvement in credit scoring accuracy causes significant loss reduction, then the utilization of best classification model is of a great importance. A real dataset was used to test the model and classifiers. The test results showed that proposed hybrid ensemble model has better classification accuracy and performance when compared to other credit scoring methods. In addition, among three classifiers, the support Vector Machine had the best performance and accuracy.

Artificial Intelligence and Bank Soundness: A Done Deal? - Part 1

“…Banks apply credit scoring to issue loans, make investment and risk management decisions. As credit risk is evaluated through credit scoring, the accuracy of credit scoring is necessary for bank's earning as even a 1% improvement in the accuracy of prediction could lead to significant decrease in losses to financial institutions [26]. As AI supersedes traditional statistical scoring models with its ability to work with big data, nonlinear relationships, improving accuracy of prediction [26,27] and thus, a better evaluator and predictor of credit risk, reducing significant losses from non-performing loans [26,[28][29][30].…”

Measuring Credit Risk of Bank Customers Using Artificial Neural Network

“…The aim of credit rating is to categorize the applicants into two groups; applicants with good credit and applicants with bad credit (Ghodselahi & Amirmadhi, 2011). Multilayer feedforward networks are a class of universal approximation (Hornik, Stinchcombe, & White, 1989).…”

Business Analytics using Random Forest Trees for Credit Risk Prediction: A Comparison Study

“…The authors in [17] investigated the suitability of several AI techniques in predicting credit risk. Ten classification algorithms were used to predict the credit risk in a German dataset.…”

“…Support Vector Machines [6] Artificial Neural Networks [7,8] Feature Selection Based using ANN [9] Ensemble ANN [10] ANN and Decision Tables [11] Evolutionary Product-ANN [12] Fuzzy Immune Learning [13] Genetic Programming [14] Genetic Programming and SVM [15] Wavelet Networks and Particle Swarm Optimization [16] Various AI Techniques [17,18] ML homogenous and hybrid approaches show promising results; nevertheless they do not overperform simpler approaches significantly. Per contra, simpler approaches ask for attention with an opportunity for efficient prediction performance.…”