Credit Risk Management in the Financial Sector Using Machine Learning as a Tool: A Systematic Literature Review

Espinoza Alvarado K.; Vallenas Santillan R.; Baca Marroquin E. · Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology (2024) · DOI: 10.18687/laccei2024.1.1.1097

Resumen

Several techniques are used in Credit Risk Management in the financial sector. Among them, the Machine Learning tool is different and innovative. Many companies have started to ignore traditional algorithms and look for more modern tools that present challenges. In this systematic literature review (SLR), the PRISMA methodology was used to identify the 20 selected papers, the results of which were obtained to address the topic of SLR. In this sense, it has been identified that in the last 5 years, a considerable number of research addresses the topic of credit risk management using Machine Learning as a tool within the financial sector. In summary, Machine Learning is a tool capable of accurately predicting credit risks, based on the client's financial data information compared to the accuracy obtained from traditional algorithms. However, its use also generates certain disadvantages, such as the high hardware and software implementation costs and other complexities generated in the implementation process.

Instituciones: Universidad Tecnológica del Perú