A Review on Financial Fraud Detection using AI and Machine Learning
Open accessAbstract
This study thoroughly explores advanced approaches for addressing financial fraud, focusing on the effectiveness of Machine Learning (ML) and Artificial Intelligence (AI). Recognizing the drawbacks of outdated methods, the examination aims to analyze the current situation, closely examining the efficiency and limitations of ML and AI techniques while mapping out intricate directions for future research. We delve into the intricate history of financial fraud, uncovering the inherent constraints embedded in conventional rule-based and manual detection approaches. Then, machine learning (ML) and artificial intelligence (AI) are discussed, highlighting significant research and successful implementations that have transformed the field of fraud detection. While analyzing the assessment metrics, we use various measures such as accuracy, precision, recall, F1 score, and the enigmatic ROC-AUC. Then, diverse ML and AI algorithms are introduced, including the mysterious Random Forest, the reliable Support Vector Machines (SVM), and the complex neural networks. As comparative analysis unfurls, uncovering the strengths and weaknesses inherent in distinct ML and AI systems. Beyond the limits of performance measures, our interpretation transcends, diving into the real-world ramifications and the labyrinth of possible routes for refinement and advancement.
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Unsupervised and hybrid models may have particular appeal for developing countries and emerging markets due to the lack of labeled fraud data.
“Similar to previous research (Hassija et al. 2024; Kamuangu, 2024), both unsupervised and hybrid models may have particular appeal for developing countries and emerging markets due to the lack of fraud data with labels.”
From AI-driven financial fraud detection in Pakistan’s banking sector: bridging strategic intent and operational implementation (2026) · cites this paper as Kamuangu, 2024
Section 3.2 says unsupervised fraud-detection methods work without labeled examples, supporting their appeal when labeled data are scarce. The hybrid-model discussion in Section 3 does not link hybrid models to a lack of labels, and the cited passages do not identify developing countries or emerging markets as a particular setting for either approach. Page numbers are unavailable in our copy. (LLM, conf=0.85)
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References 29 · Cited by 61
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- Authors
- Kamuangu, P.
- Affiliations
- Liberty University (US)
- Corresponding author
- Kamuangu, Paulin
- ORCID
- Kamuangu, Paulin
- Venue
- Journal of Economics, Finance and Accounting Studies
- Published
- 2024-02-11
- Volume
- 6
- Issue
- 1
- Pages
- 67-77
- ISSN
- 2709-0809 (ISSN-L)
- OpenAlex
- W4391734419
- Cited by
- 61
- References
- 29
- Citations by year
- 2026: 27 · 2025: 26 · 2024: 7 · 2023: 1
- FWCI
- 8.89
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Journal of Economics Finance and Accounting Studies (publishedVersion, cc-by-nc)
- Figshare (submittedVersion, cc-by)
- Figshare (cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Benford’s Law and Fraud Detection
- Social Sciences › Business, Management and Accounting › Accounting › Financial Distress and Bankruptcy Prediction
- Keywords
- financial fraud detection, machine learning, artificial intelligence, random forest, support vector machine, artificial neural networks, performance indicators, area under the receiver operating characteristic, algorithm comparison
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