Abstract
Loans are financing services for clients of a bank and are one of the main activities in a financial institution since they are the means through which they make money. When a customer misses one or more payments cause grave problems at the bank at the point of crash. The bank loan manager to decide decides whether to approve or not the loan application using the client’s financial and personal information. This decision always has associated risks. Currently, financial institutions, to reduce the risks associated with loan approval and take advantage of the large repositories of historical data from their clients, are using machine learning algorithms to identify if a client will comply with the loan payment. That information helps managers in their decision-making process. This paper presents the development of an application to support the process of authorizing or not a bank loan in the Acción Imbaburapak Savings and credit cooperative; to choose the model to use in the application, select after training three predictive methods. The analytical process followed the phases proposed by the KDD methodology. Three supervised classification methods were selected: logistic regression, decision trees, and neural networks. Since the neural network showed the best results during the evaluation, we chose this to build the application.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Lai, L.: Loan default prediction with machine learning techniques. In: 2020 International Conference on Computer Communication and Network Security (CCNS), pp. 5–9. IEEE (2020)
Tan, Z., Yan, Z., Zhu, G.: Stock selection with random forest: An exploitation of excess return in the Chinese stock market. Heliyon 5(8), e02310 (2019)
Aslam, U., et al.: An empirical study on loan default prediction models. J. Comput. Theor.Nanosci. 16(8), 3483–3488 (2019)
Alsaleem, M.Y., Hasoon, S.O.: Predicting bank loan risks using machine learning algorithms. AL-Rafidain J. Comput. Sci. Math. 14(1), 149–158 (2020)
Nazemi, A., Heidenreich, K., Fabozzi, F.J.: Improving corporate bond recovery rate prediction using multi-factor support vector regressions. Eur. J. Oper. Res. 271(2), 664–675 (2018)
Paredes, J.S.V.: Análisis de riesgo para préstamos bancarios. ILUMINATE, 47–60 (2020)
Gupta, R., et al.: The predictive value of inequality measures for stock returns: An analysis of long-span UK data using quantile random forests. Finan. Res. Lett. 29, 315–322 (2019)
Pławiak, P., Abdar, M., Rajendra Acharya, U.: Application of new deep genetic cascade ensemble of SVM classifiers to predict the Australian credit scoring. Appl. Soft Comput. J. 84, 105–740 (2019)
Soleymani, F., Masnavi, H., Shateyi, S.: Classifying a lending portfolio of loans with dynamic updates via a machine learning technique. Mathematics 9(1), 17 (2021)
Barua, S., Gavandi, D., Sangle, P., Shinde, L., Ramteke, J.: Swindle: Predicting the probability of loan defaults using catboost algorithm. In: 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), pp. 1710–115. IEEE (2021)
Levanon, S., Rosenfeld, N.: Strategic classification made practical. In: International Conference on Machine Learning, pp. 6243–6253. PMLR (2021)
Ghalme, G., Nair, V., Eilat, I., Talgam-Cohen, I., Rosenfeld, N.: Strategic classification in the dark. In: International Conference on Machine Learning, pp. 3672–3681. PMLR (2021)
Wang, D.N., Li, L., Zhao, D.: Corporate finance risk prediction based on LightGBM. Inf. Sci. 602, 259–268 (2022)
Zhu, Q., Ding, W., Xiang, M., Hu, M., Zhang, N.: Loan default prediction based on convolutional neural network and LightGBM. Int. J. Data Warehouse Min. (IJDWM). 19, 1–16 (2023)
Braga, L.P.V., Valencia, L.I.O., Carvajal, S.S.R.: Introducción a la Minería de Datos. Editora E-papers (2009)
IBM. https://www.ibm.com/es-es/topics/decision-trees#:~:text=Un%20%C3%A1rbol%20de%20decisi%C3%B3n%20es,nodos%20internos%20y%20nodos%20hoja. Accessed 05 Feb 2022
IA.net Homepage. https://www.iartificial.net/random-forest-bosque-aleatorio. Accessed 31 Jan 2022
Fernández, A.O.N.: Redes Neuronales (2021)
Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery in databases. AI Mag. 17(3), 37 (1996)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Rivero, D., Guerra, L., Narváez, W., Arcinegas, S. (2024). A Tool to Predict Payment Default in Financial Institutions. In: Florez, H., Leon, M. (eds) Applied Informatics. ICAI 2023. Communications in Computer and Information Science, vol 1874. Springer, Cham. https://doi.org/10.1007/978-3-031-46813-1_13
Download citation
DOI: https://doi.org/10.1007/978-3-031-46813-1_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-46812-4
Online ISBN: 978-3-031-46813-1
eBook Packages: Computer ScienceComputer Science (R0)