Credit Risk Management in the Financial Sector Using Machine Learning as a Tool: A Systematic Literature Review (#1097)

Authors

Espinoza Alvarado, Kevin

Vallenas Santillán, Renzo

Baca Marroquin, Emily

Abstract

Several techniques are used in Credit Risk Management in the financial sector. Among them, Machine Learning is a different and innovative tool. Many companies have started ignoring traditional algorithms and looking for more modern tools that present some challenges. In this Systematic Literature Review (SLR), the PRISMA methodology was used to identify the 20 selected papers from which results have been obtained to address the topic of SLR. In this sense, it has been identified that in the last 5 years, a considerable amount of research has addressed the topic of credit risk management using machine learning as a tool within the financial sector. In summary, Machine Learning turns out to be a tool capable of predicting credit risks with high accuracy, based on the information of the client's financial data compared to the accuracy obtained from the use of 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.