Challenges of Artificial Intelligence for the Prevention and Identification of Bankruptcy Risk in Financial Institutions: A Systematic Review

Luis-Javier Vásquez-Serpa (), Ciro Rodríguez, Jhelly-Reynaluz Pérez-Núñez and Carlos Navarro
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Luis-Javier Vásquez-Serpa: Facultad de Ingeniería de Sistemas e Informática, Universidad Nacional Mayor de San Marcos (UNMSM), Lima 15081, Peru
Ciro Rodríguez: Facultad de Ingeniería de Sistemas e Informática, Universidad Nacional Mayor de San Marcos (UNMSM), Lima 15081, Peru
Jhelly-Reynaluz Pérez-Núñez: Facultad de Ingeniería de Sistemas e Informática, Universidad Nacional Mayor de San Marcos (UNMSM), Lima 15081, Peru
Carlos Navarro: Facultad de Ingeniería de Sistemas e Informática, Universidad Nacional Mayor de San Marcos (UNMSM), Lima 15081, Peru

JRFM, 2025, vol. 18, issue 1, 1-34

Abstract: The identification and prediction of financial bankruptcy has gained relevance due to its impact on economic and financial stability. This study performs a systematic review of artificial intelligence (AI) models used in bankruptcy prediction, evaluating their performance and relevance using the PRISMA and PICOC frameworks. Traditional models such as random forest, logistic regression, KNN, and neural networks are analyzed, along with advanced techniques such as Extreme Gradient Boosting (XGBoost), convolutional neural networks (CNN), long short-term memory (LSTM), hybrid models, and ensemble methods such as bagging and boosting. The findings highlight that, although traditional models are useful for their simplicity and low computational cost, advanced techniques such as LSTM and XGBoost stand out for their high accuracy, sometimes exceeding 99%. However, these techniques present significant challenges, such as the need for large volumes of data and high computational resources. This paper identifies strengths and limitations of these approaches and analyses their practical implications, highlighting the superiority of AI in terms of accuracy, timeliness, and early detection compared to traditional financial ratios, which remain essential tools. In conclusion, the review proposes approaches that integrate scalability and practicality, offering predictive solutions tailored to real financial contexts with limited resources.

Keywords: bank bankruptcy risk; financial institution; bank; artificial intelligence; prediction; machine learning; random forest; CNN; LSTM; XGBoost; accuracy; precision (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2025
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