Abstract
This chapter illustrates a prediction of the loan applicants' timely payments with optimization. A neural networks tool is used to predict unknown values of categorical dependent variables from known values of numeric and categorical independent variables. In this model, a neural net learns to predict whether an auto loan applicant will make timely payments, late payments, or default on the loan. The data contains information on applicants who took car loans in the past. The input data of five new applicants is also given. It is supposed that the bank executives want to allocate a certain amount of money in loans to the five applicants to minimize the probability of a default occurring. Therefore, Neural Networks and optimization tools are used to predict the optimal values for the new applicants.
Reference5 articles.
1. Addo, P. M. (2018). Credit Risk Analysis Using Machine and DeepLearning Models, Risks. MDPI. https://res.mdpi.com/d_attachment/risks/risks-06-00038/article_deploy/risks-06-00038-v2.pdf
2. Gately, E. (1995). Neural Networks for Financial Forecasting (1st ed.). Wiley.
3. Srivastava, S. (2020). Loan Default Prediction Using Artificial Neural Networks. International Journal of Advanced Science and Technology, 29(6). http://sersc.org/journals/index.php/IJAST/article/view/13756
4. Trippi, R. R., & Turban, E. (Eds.). (1992). Neural Networks in Finance and Investing: Using Artificial Intelligence to Improve Real World Performance. McGraw-Hill.
5. The consumer loan default predicting model – An application of DEA–DA and neural network