Abstract
The present research explores the possibility of using neural networks to predict credit risk in the banking sector through a case study of a database of one of the American banks. Scoring is a mathematical or statistical model used by a bank to determine, based on the credit history of “past” clients, how likely it is that a particular potential borrower will repay the loan on time. A scoring model is a weighted sum of certain characteristics. The result is an integrated parameter (score); the higher it is, the more reliable the client is, and the bank can order the clients according to their level of creditworthiness in increasing order.
The integrated parameter of each client is compared with a certain numerical threshold, or boundary line, which is essentially a break-even line and is obtained from the reckoning of the average number of clients paying on time needed to compensate for losses from a single debtor. Clients with an integrated parameter above this line are given credit, while clients with an integrated parameter below this line are not.
Theoretical aspects of the neural network application were considered. A basic table of real data on the bank’s clients was studied. Based on the results of the study, conclusions were made that helped solve the problem of building a neural network.
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Ivanyuk, V., Slovesnov, E., Soloviev, V. (2021). Credit Risk Assessment in the Banking Sector Based on Neural Network Analysis. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2021. Lecture Notes in Computer Science(), vol 12855. Springer, Cham. https://doi.org/10.1007/978-3-030-87897-9_25
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