ADAPTIVE AI ALGORITHMS FOR EARLY DETECTION OF EMERGING FINANCIAL FRAUD SCHEMES
Bibliographic record
Abstract
Financial fraud continues to evolve in both complexity and scale, frequently outpacing the capabilities of static, rule-based detection systems traditionally deployed in the financial sector.These conventional systems, while effective against known fraud patterns, often struggle to identify novel and sophisticated schemes that emerge in dynamic digital environments.In response to this growing challenge, this study designs and evaluates adaptive Artificial Intelligence (AI) algorithms capable of continuous learning from live transactional data streams.These algorithms can adapt in near realtime to detect emerging fraud schemes that deviate from historical norms.We adopt a mixed-methods research framework that integrates both quantitative and qualitative components.The quantitative component involves controlled experiments on anonymized transactional datasets, assessing the performance of adaptive models such as online isolation forests and reinforcement learning-based detectors.Key performance indicators include precision, recall, detection latency, and model robustness under concept drift.The qualitative component consists of semi-structured interviews with domain experts' fraud analysts, compliance officers, and AI practitioners which provide insights into the practical deployment, limitations, and expectations surrounding adaptive AI systems in operational environments.Our findings demonstrate that adaptive models significantly outperform static counterparts Srikumar Nayak https://iaeme.com/
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".