− HEALTH We are the last generation that will influence what wellbeing means before AI systems optimize that definitio…− HEALTH Abstract There is much discussion of the false outputs that generative AI systems such as ChatGPT, Claude, Ge…± SCIENCE This article presents a Delphi consensus developed by a panel of editors-in-chief of anaesthesiology and pain…+ EDUCATION The study investigated how innovative, AI-supported teaching methods relate to the mathematics problem-solvin…+ EDUCATION As generative AI becomes increasingly integrated into higher education, understanding how students engage wit…+ HEALTH In the complex and multidimensional field of medicine, multimodal data are prevalent and crucial for informed…± SCIENCE This paper explores the evolution of geoscientific inquiry, tracing the progression from traditional physics-…− POLICY This paper conducts a systematic review and interdisciplinary analysis of the ethical challenges of generativ…
TruaceTracing the truth around AIMonday, July 20, 2026
Subscribe
Crime·G Space·Evidence-backed gain·Published 2026-07-20

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

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 suc…

TRV-2026-0366Peer-reviewedPermanent record — cite & verify
Financial fraud detection through the application of machine learning techniques: a literature review

Lock on gas pump to stop thieves from installing a skimmer device by The Eloquent Peasant. CC BY-SA 4.0 · https://creativecommons.org/licenses/by-sa/4.0

The quick read

Published September 3, 2024, this peer-reviewed literature review applied PRISMA and Kitchenham methods to 104 articles from 2012-2023 obtained from Scopus, IEEE Xplore, Taylor & Francis, SAGE, and ScienceDirect to map how machine learning is used to detect financial fraud and its types.

The synthesis matters because it shows where detection research concentrates and where gaps remain: credit card fraud models dominate, real datasets are favored while synthetic data accounts for less than 7%, and contributions cluster in China, India, Saudi Arabia, and Canada with few publications from Latin America, leaving questions about generalizability across regions and fraud types.

Main points
  • PRISMA and Kitchenham methods applied to 104 articles published between 2012 and 2023 from Scopus, IEEE Xplore, Taylor & Francis, SAGE, and ScienceDirect.
  • Analysis indicated a trend toward using real datasets, with synthetic data use below 7% of employed datasets.
  • Data sources included stock exchanges of China, Canada, United States, Taiwan, and Tehran, with leading contributors from China, India, Saudi Arabia, and Canada.
Gain

Literature shows machine learning models are applied to detect financial fraud, with credit card fraud detection models most widely used.

The rundown

The review examined 104 articles from 2012 to 2023 selected via predefined inclusion and exclusion criteria, extracting information on authors, sources, countries, trends, and datasets used in experiments.

Findings highlight that credit card fraud detection models dominate the literature, real datasets are increasingly preferred over synthetic, and data were sourced from exchanges in China, Canada, the United States, Taiwan, and Tehran among others.

Sources

Reader signal

How should this claim be treated?

The debate