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Financial fraud detection through the application of machine learning techniques: a literature review

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Publicado en:Humanities & Social Sciences Communications vol. 11, no. 1 (Dec 2024), p. 1130
Autor principal: Hernandez Aros, Ludivia
Otros Autores: Bustamante Molano, Luisa Ximena, Gutierrez-Portela, Fernando, Moreno Hernandez, John Johver, Rodríguez Barrero, Mario Samuel
Publicado:
Springer Nature B.V.
Materias:
PricewaterhouseCoopers LLP
Latin America
Machine learning
Research methodology
Accuracy
Datasets
Bibliometrics
Trends
Data mining
Experiments
Bank fraud
Fraud prevention
Financial statements
Literature reviews
Systematic review
Databases
Profits
Fraud
Owners
Stock exchanges
Investors
Literary criticism
Credit
Credit cards
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Resumen:Financial fraud negatively impacts organizational administrative processes, particularly affecting owners and/or investors seeking to maximize their profits. Addressing this issue, this study presents a literature review on financial fraud detection through machine learning techniques. The PRISMA and Kitchenham methods were applied, and 104 articles published between 2012 and 2023 were examined. These articles were selected based on predefined inclusion and exclusion criteria and were obtained from databases such as Scopus, IEEE Xplore, Taylor & Francis, SAGE, and ScienceDirect. These selected articles, along with the contributions of authors, sources, countries, trends, and datasets used in the experiments, were used to detect financial fraud and its existing types. Machine learning models and metrics were used to assess performance. The analysis indicated a trend toward using real datasets. Notably, credit card fraud detection models are the most widely used for detecting credit card loan fraud. The information obtained by different authors was acquired from the stock exchanges of China, Canada, the United States, Taiwan, and Tehran, among other countries. Furthermore, the usage of synthetic data has been low (less than 7% of the employed datasets). Among the leading contributors to the studies, China, India, Saudi Arabia, and Canada remain prominent, whereas Latin American countries have few related publications.
ISSN:2662-9992
2055-1045
DOI:10.1057/s41599-024-03606-0
Fuente:Social Science Database