Financial Fraud: A Review of Anomaly Detection Techniques and Recent Advances
Bibliographic record
Abstract
With the rise of technology and the continued economic growth evident in modern society, acts of fraud have become much more prevalent in the financial industry, costing institutions and consumers hundreds of billions of dollars annually. Fraudsters are continuously evolving their approaches to exploit the vulnerabilities of the current prevention measures in place, many of whom are targeting the financial sector. These crimes include credit card fraud, healthcare and automobile insurance fraud, money laundering, securities and commodities fraud and insider trading. On their own, fraud prevention systems do not provide adequate security against these criminal acts. As such, the need for fraud detection systems to detect fraudulent acts after they have already been committed and the potential cost savings of doing so is more evident than ever. Anomaly detection techniques have been intensively studied for this purpose by researchers over the last couple of decades, many of which employed statistical, artificial intelligence and machine learning models. Supervised learning algorithms have been the most popular types of models studied in research up until recently. However, supervised learning models are associated with many challenges that have been and can be addressed by semi-supervised and unsupervised learning models proposed in recently published literature. This survey aims to investigate and present a thorough review of the most popular and effective anomaly detection techniques applied to detect financial fraud, with a focus on highlighting the recent advancements in the areas of semi-supervised and unsupervised learning.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".