Keywords: credit risk, backpropagation neural networks, BPNN, decision trees, logistic regression, Monte Carlo simulation, data mining, risk assessment, credit status, credit scoring
Demonstration of data mining approaches in credit risk evaluation
Ratios from financial statements provide useful information to describe credit conditions from various perspectives, such as financial conditions and credit status. A good quantitative model is crucial in scoring the company credit status. This study demonstrates the utilisation of several data mining algorithms, i.e., Backpropagation Neural Network (BPNN), decision tree, logistic regression and Monte Carlo simulation in credit-scoring problem. Results are favourable in this case study.