Financial Fraud Detection in the Banking Sector Using Machine Learning: An Exhaustive Systematic Review

Authors

  • Orlando Jeri-Alvarado Universidad Nacional Federico Villarreal
  • Cristopher Espinoza Universidad Nacional Federico Villarreal
  • Javier Gamboa-Cruzado Universidad Nacional Mayor de San Marcos
  • María León Morales Universidad Nacional de Cajamarca
  • Victor Ataupillco-Vera Universidad Nacional Mayor de San Marcos
  • Oscar Chávez-Chavez Universidad Nacional Mayor de San Marcos
  • Carlos Andrés Tavera Romero Universidad Santiago de Cali
  • Francisco Antonio Castillo-Velázquez Universidad Politécnica de Querétaro

DOI:

https://doi.org/10.13053/cys-29-3-5909

Keywords:

Machine learning, financial fraud detection, banking sector, deep learning, identification of financial scams, banking system, systematic review

Abstract

In recent years, the application of machine learning techniques for detecting financial fraud within the banking sector has experienced a remarkable increase. This paper seeks to highlight this progress and emphasize its impact on enhancing fraud prevention and control systems. The objective of this paper is to explore, determine, and identify the current state of knowledge regarding the use of machine learning in financial fraud detection in the banking sector. This study was based on 61 papers selected from six major digital libraries: IEEE Xplore, Scopus, ScienceDirect, ProQuest, ARDI, and Web of Science. Only peer-reviewed journal papers were included. The systematic review covered publications between 2019 and 2025, available in openaccess databases, focusing on the use of machine learning in detecting financial fraud in the banking sector. The findings from the 61 reviewed papers indicate that the most widely used programming language for machine learning solutions is Scala. Additionally, tools implemented in fraud detection and gaps in model comparison were identified. It is recommended to explore more recent approaches and banking contexts that have not yet been addressed.

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Published

2025-09-26

Issue

Vol. 29 No. 3 (2025): Vol 29 No 3 (2025)

Section

Articles

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