Prediction of credit default risk (scoring): Review of existing methods
With the development of a market economy, lending to both individuals and legal entities began to actively develop. However, the risk to which a credit institution is exposed is quite high. To reduce it recently, various mathematical methods have been actively used to evaluate the activities of their client according to various criteria. Moreover, it is important to note that the less any costs are required for such a mathematical apparatus, the more actively it is developed and applied. This article provides a comprehensive overview of known credit scoring methods. The main works are presented, conclusions from them on the accuracy of the methods are drawn. A comparative analysis of each method is presented in the paper. Despite such a seemingly large number of existing methods exists, there are still interesting points regarding the improvement of credit scoring methods. First, the process of preparing data, such as collecting data, selecting variables (factors) and identifying more informative ones for predicting credit scoring can help reduce noise and further improve the accuracy of credit risk assessment. However, most studies at the moment neglected this circumstance. Secondly, due to the fact that the combination of modern methods (for example, genetic programming with neural networks) only recently began to be considered and demonstrated their superiority over others, this device has not yet been fully developed and requires additional research.