Credit risk analysis : using artificial intelligence in a web application

• Autores: Cano Lengua, Miguel Ángel, Andrade Arenas, Laberiano, Mayorga Lira, Sergio Dennis
• Formato: artículo
• Fecha de Publicación: 2023
• Institución: Universidad Tecnológica del Perú
• Repositorio: UTP-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.utp.edu.pe:20.500.12867/6879
• Enlace del recurso: https://hdl.handle.net/20.500.12867/6879; http://doi.org/10.14445/22315381/IJETT-V71I1P227
• Nivel de acceso: acceso abierto
• Materia: Artificial intelligence; Credit risk; Financial entity; Machine learning; https://purl.org/pe-repo/ocde/ford#1.02.00

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.