IJBTT

Credit Risk Analysis : Using Artificial Intelligence in a Web Application

Credit Risk Analysis : Using Artificial Intelligence in a Web Application
	© 2023 by IJETT Journal
	Volume-71 Issue-1
	Year of Publication : 2023
	Author : Mayorga Lira Sergio Dennis, Laberiano Andrade-Arenas, Miguel Angel Cano Lengua
	DOI : 10.14445/22315381/IJETT-V71I1P227

How to Cite?

Mayorga Lira Sergio Dennis, Laberiano Andrade-Arenas, Miguel Angel Cano Lengua, "Credit Risk Analysis : Using Artificial Intelligence in a Web Application," International Journal of Engineering Trends and Technology, vol. 71, no. 1, pp. 305-316, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I1P227

Abstract
The advantages of machine learning are not only in trying to reduce losses due to better prediction but there are also benefits related to the evaluation of risk profiles, whether they are clients or entities. It also adds to the savings in operating costs and resources that must be reserved to cover potential delinquency. The objective of the work is to imply that artificial intelligence can help measure the credit risk index of a financial institution to avoid loss and thus determine whether to access a loan or not. In the methodology, the Python programming language will be used with the necessary libraries for the analysis of Artificial Intelligence (AI), which, through the steps done in work, will proceed to make an application that demonstrates how useful it is. It is machine learning to avoid losses. Finally, the final result obtained will be the application which shows us if a client accesses a bank loan or if, on the contrary, it was rejected based on old clients.

Keywords
Artificial intelligence, Financial entity, Credit risk, Machine learning, Python programming language.

References
[1] Chinchay Martinez, Paky milagros, and Milagros Yadira Lozada Celi, “Applications of Artificial Intelligence in Business Strategy,” Peruvian University of Applied Sciences, 2020. [Online]. Available: http://hdl.handle.net/10757/652935
[2] Maria Del Carmen Sosa Sierra, “Artificial Intelligence in Business Financial Management,” Thought & Management, no. 23, pp. 153– 186, 2007.
[3] D. C. Paucar Sulka, “Evaluation of Credit Risks in the Company Mi Banco Sa–Agencia Chupaca, Through Neural Networks,” Peruvian University of Applied Sciences, 2018. [Online]. Available: http://hdl.handle.net/20.500.12894/4914
[4] Gonzalo Pimentel Ramirez, "Artificial Intelligence and its Influence on the Efficiency of International Trade, 2020,” 2020. [Online]. Available: https://repositorio.usil.edu.pe/handle/usil/9776
[5] Flores Limaylla, Guadalupe Isabel, and Pena Alvarez, Eddy Paolo, “Machine Learning for the Optimization of Digital Marketing Processes in the Tourism Sector,” 2020. [Online]. Available: https://hdl.handle.net/20.500.12867/3430
[6] Ana Fernández, “Artificial Intelligence in Financial Services,” Economic Bulletin, no. 6, pp. 1 – 10, 2019. [Online]. Available: https://ideas.repec.org/a/bde/joures/y2019i6daan7.html
[7] Héctor Eduardo Díaz Rodríguez, Magnolia Miriam Sosa Castro, and María Alejandra Cabello Rosales, “Financial Performance and Administrative Practices in Mexican Microenterprises: An Analysis with Artificial Neural Networks,” Accounting and administration, vol. 64, no. 3, 2019. Crossref, https://doi.org/10.22201/fca.24488410e.2018.1622
[8] Barreto Rodriguez, Robert Marcos and Lizarraga Mendoza, David Jesus, “Facial Recognition System Model for the Control of Human Trafficking,” 2019. [Online]. Available: https://hdl.handle.net/20.500.12867/2063
[9] Víctor Andres Bucheli Guerrero, “Development of the State of the Art in Research: A Tool Based on Artificial Intelligence,” Polytechnic Magazine, vol. 15, no. 30, pp. 70–81, 12 2019. Crossref, https://doi.org/10.33571/rpolitec.v15n30a7
[10] Dr. Hassabelrasul Yousuf AL Tom Shihabeldeen, "An Application in Islamic Financial Sector using Data Mining", SSRG International Journal of Economics and Management Studies, vol. 6, no. 11, pp. 107-111, 2019. Crossref, https://doi.org/10.14445/23939125/IJEMSV6I11P112
[11] Reiner Solís Villanueva, “Artificial Intelligence Techniques to Optimize the Efficiency of the Selection Procedure for the Contracting of Public Works,” Interfaces, no. 11, pp. 13–42, 2018. Crossref, https://doi.org/10.26439/interfases2018.n011.2951
[12] Jimenez Caballero et al., “Neural Networks in Their Application to Finance,” Banking & Finance, vol. 54, 19-26, 2000. [Online]. Available: http://hdl.handle.net/11441/24855
[13] Manuel Rodriguez Lopez, Carlo Pineiro Sánchez, and Pablo de Llano Monelos, “Determining the Risk of Financial Failure Through the Use of Parametric Models, Artificial Intelligence, and Audit Information,” Economics studies, vol. 41, no. 2, pp. 187–217, 2014. Crossref, https://doi.org/10.4067/S0718-52862014000200002
[14] Kareem Mohamed and Ummü Altan Bayraktar, "Artificial Intelligence in Public Relations and Association Rule Mining as a Decision Support Tool," SSRG International Journal of Humanities and Social Science, vol. 9, no. 3, pp. 23-32, 2022. Crossref, https://doi.org/10.14445/23942703/IJHSS-V9I3P105
[15] Martha Condori Quispe, “Neural Network Model for the Simulation of Credit Risk of SMEs in the city of La Paz,” Ph.D. Dissertation, 2011. [Online]. Available: http://hdl.handle.net/123456789/1366
[16] Bruno Lopez Takeyas, “Introduction to Artificial Intelligence,” Technological Institute of Nuevo Laredo, 2007. [Online]. Available: http://www. itnuevolaredo. edu. mx/takeyas, 2007.
[17] Alfonso Moreno Rodríguez, “Development of a Graphical Interface of Neural Networks Using Matlab,” Master’s Thesis, 2009. [Online]. Available: http://hdl.handle.net/10016/8488
[18] Vimal Kumar Stephen et al., "Challenges of Implementing Industry 4.0 In Financial Sector of Oman," SSRG International Journal of Economics and Management Studies, vol. 9, no. 1, pp. 35-38, 2022. Crossref, https://doi.org/10.14445/23939125/IJEMS-V9I1P106
[19] Douglas R. Emery, John D. Finnerty, and John D. Stowe, “Financial Management Fundamentals,” Pearson Education, 2000. [Online]. Available: https://books.google.com.pe/books?id=isR9DyNXdDwC
[20] Gil Vera, and Víctor Daniel, “Development of an Artificial Neural Network for the Identification of Potential Delinquent Debtors, QUID: Investigación, Ciencia y Tecnología, no. 24, pp. 29–34, 2015.
[21] Dario Utrilla Salazar, “Multisensory Architecture System for Supervision and Industrial Security Applying Artificial Intelligence Technology,” 2014. [Online]. Available: https://hdl.handle.net/20.500.12672/3870
[22] Fredy Ocaris Perez Ramírez and Horacio Fernandez Castano, “Neural Networks and Credit Risk Assessment,” Engineering Magazine University of Medellín, vol. 6, no. 10, pp. 77–91, 2007.
[23] I. Zumárraga Eguidazu, “Redes Neuronales En Matlab,” 2019.