AI Driven Fraud Detection Models in Financial Networks: A Comprehensive Systematic Review
Bibliographic record
Abstract
Rapid advancements in digital innovation and globalization has significantly increased the complexity of financial networks, making them more vulnerable to fraud. Traditional fraud detection methods struggle to keep pace with evolving fraudulent strategies, contributing to an estimated global financial loss of $ 5 trillion. In response, this review paper explores the role of artificial intelligence (AI) in financial fraud detection, highlighting machine learning (ML), deep learning (DL), and hybrid models as transformative solutions. By analyzing vast datasets, AI can uncover hidden fraud patterns and dynamically adapt to emerging threats. Techniques such as supervised and unsupervised learning, along with advanced approaches like Graph Neural Networks (GNNs), have proven particularly effective in detecting various types of financial fraud, including payment fraud, identity theft, and money laundering. This paper presents a comprehensive taxonomy of AI-driven fraud detection methodologies, synthesizing insights from a substantial number of research papers. It systematically categorizes fraud detection techniques based on their application in different types of fraud, providing a structured framework to understand their effectiveness. In addition, it examines the role of cloud computing, edge AI, and distributed systems in enabling real-time transaction monitoring and fraud detection. Although AI significantly improves detection accuracy, reduces operational costs, and strengthens regulatory compliance, challenges such as model explainability, data privacy concerns, algorithmic bias, and the dynamic nature of fraud remain critical barriers to widespread adoption. Our review highlights the need for collaborative efforts among financial institutions, regulators, and technology providers to address these challenges. Future research should focus on improving the transparency of the AI model, integrating AI with blockchain for secure data sharing, and leveraging federated learning to enhance fraud detection capabilities. By addressing these challenges, AI can play a pivotal role in securing financial systems, minimizing fraud risks, and fostering cross-industry collaboration for more resilient fraud detection frameworks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".