Abstract

ESPINOZA, Frank Edward Tadeo  and  YGNACIO, Marco Antonio Coral. Credit Risk Assessment Models in Financial Technology: A Review. TecnoL. [online]. 2023, vol.26, n.58, e302.  Epub Mar 04, 2024. ISSN 0123-7799.  https://doi.org/10.22430/22565337.2679.

This review analyzes a selection of scientific articles on the implementation of Credit Risk Assessment (CRA) systems to identify existing solutions, the most accurate ones, and limitations and problems in their development. The PRISMA statement was adopted as follows: the research questions were formulated, the inclusion criteria were defined, the keywords were selected, and the search string was designed. Finally, several descriptive statistics of the selected articles were calculated. Thirty-one solutions were identified in the selected studies; they include methods, models, and algorithms. Some of the most widely used models are based on Artificial Intelligence (AI) techniques, especially Neural Networks and Random Forest. It was concluded that Neural Networks are the most efficient solutions, with average accuracies above 90 %, but their development can have limitations. These solutions should be implemented considering the context in which they will be employed.

Keywords : Credit assessment; credit risk; technology solutions; machine learning; algorithms.

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