Credit Scoring Model Based on the Decision Tree and the Simulated Annealing Algorithm

Credit scoring models have been widely studied in academic world and the business community. Many novel approaches such as artificial neural networks (ANNs), rough sets, or decision trees have been proposed to increase the accuracy of credit scoring models. The C4.5 is a learning algorithm which adopts local search strategy, it cannot obtain the best decision rules. On the other hand, the simulated annealing algorithm is a global optimized algorithm, it avoids the drawbacks of C4.5. This paper proposes a new credit scoring model based on decision tree and simulated annealing algorithm. The experimental results demonstrate that the proposed model is effective.