Financial fraud detection through the application of machine learning techniques: a literature review
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| Udgivet i: | Humanities & Social Sciences Communications vol. 11, no. 1 (Dec 2024), p. 1130 |
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| Hovedforfatter: | |
| Andre forfattere: | , , , |
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Springer Nature B.V.
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| Online adgang: | Citation/Abstract Full Text Full Text - PDF |
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| 001 | 3100376995 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2662-9992 | ||
| 022 | |a 2055-1045 | ||
| 024 | 7 | |a 10.1057/s41599-024-03606-0 |2 doi | |
| 035 | |a 3100376995 | ||
| 045 | 2 | |b d20241201 |b d20241231 | |
| 100 | 1 | |a Hernandez Aros, Ludivia |u Universidad Cooperativa de Colombia, School of Public Accounting, Ibagué, Colombia (GRID:grid.442158.e) (ISNI:0000 0001 2300 1573) | |
| 245 | 1 | |a Financial fraud detection through the application of machine learning techniques: a literature review | |
| 260 | |b Springer Nature B.V. |c Dec 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 610 | 4 | |a PricewaterhouseCoopers LLP | |
| 651 | 4 | |a Latin America | |
| 653 | |a Machine learning | ||
| 653 | |a Research methodology | ||
| 653 | |a Accuracy | ||
| 653 | |a Datasets | ||
| 653 | |a Bibliometrics | ||
| 653 | |a Trends | ||
| 653 | |a Data mining | ||
| 653 | |a Experiments | ||
| 653 | |a Bank fraud | ||
| 653 | |a Fraud prevention | ||
| 653 | |a Financial statements | ||
| 653 | |a Literature reviews | ||
| 653 | |a Systematic review | ||
| 653 | |a Databases | ||
| 653 | |a Profits | ||
| 653 | |a Fraud | ||
| 653 | |a Owners | ||
| 653 | |a Stock exchanges | ||
| 653 | |a Investors | ||
| 653 | |a Literary criticism | ||
| 653 | |a Credit | ||
| 653 | |a Credit cards | ||
| 700 | 1 | |a Bustamante Molano, Luisa Ximena |u Universidad Cooperativa de Colombia, School of Systems Engineering, Ibagué, Colombia (GRID:grid.442158.e) (ISNI:0000 0001 2300 1573) | |
| 700 | 1 | |a Gutierrez-Portela, Fernando |u Universidad Cooperativa de Colombia, School of Systems Engineering, Ibagué, Colombia (GRID:grid.442158.e) (ISNI:0000 0001 2300 1573) | |
| 700 | 1 | |a Moreno Hernandez, John Johver |u Universidad Cooperativa de Colombia, School of Public Accounting, Ibagué, Colombia (GRID:grid.442158.e) (ISNI:0000 0001 2300 1573) | |
| 700 | 1 | |a Rodríguez Barrero, Mario Samuel |u Universidad Cooperativa de Colombia, School of Business Administration, Ibagué, Colombia (GRID:grid.442158.e) (ISNI:0000 0001 2300 1573) | |
| 773 | 0 | |t Humanities & Social Sciences Communications |g vol. 11, no. 1 (Dec 2024), p. 1130 | |
| 786 | 0 | |d ProQuest |t Social Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3100376995/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3100376995/fulltext/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3100376995/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |