(AGH University of Science and Technology Press, 2020) Gaweł, Bartłomiej; Paliński, Andrzej
The article presents the basic techniques of data mining implemented in typical commercial software. They were used to assess the risk of credit card debt repayment. The article assesses the quality of classification models derived from data mining techniques and compares their results with the traditional approach using a logit model to assess credit risk. It turns out that data mining models provide similar accuracy of classification compared to the logit model, but they require much less work and facilitate the automation of the process of building scoring models.
This paper presents a model of bank-loan repayment as a signaling game with a set of discrete types of borrowers. The type of borrower is the return on an investment project. A possibility of renegotiation of the loan agreement leads to an equilibrium in which the borrower adjusts the repaid amount to the liquidation value of its assets (from the bank’s point of view). In the equilibrium, there are numerous pooling equilibrium points, with values rising according to the expected liquidation value of the loan. The article additionally proposes a mechanism forcing the borrower to pay all of his return instead of the common liquidation value of subset of types of the borrower. The paper contains also a simple numerical example explaining this mechanism.
