Recent Progress on Financial Risk Detection in the Context of Transaction Fraud Based on Machine Learning Algorithms
Bibliographic record
Abstract
Transaction Fraud, a type of financial operational risk, remains a major threat to financial sectors and continuously imposes devastating financial impacts. This study comprehensively reviews 41 cutting-edge publications on financial transaction fraud detection using Machine Learning from January 2018 to October 2025. We establish a taxonomy to categorize the selected work into four themes: Traditional Machine Learning, Deep Learning, Ensemble Method, and Hybrid Method. Each theme is evaluated in-depth, from strengths to weaknesses. Ensemble exhibits better performance over other methods with a recall of 92.7%, a precision of 96% and an F1-score of 92.66% on average, while Traditional ML ranks last in terms of average F1-score. Preprocessing strategies, like data balancing, can enhance performance, while feature engineering requires careful evaluation before implementation. Significantly, we assess financial implications, suggesting it is essential to integrate financial metric design, feature explanation, time series patterns, and data privacy considerations into financial fraud detection—a focus that aligns with risk management frameworks and regulations. By revealing current research gaps and suggesting future directions, our study provides practical guidance for researchers and practitioners to advance financial fraud detection strategies within a highly intricate financial ecosystem.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".