Handling Concept Drift in Fraud Detection: A Replication Study
Bibliographic record
Abstract
Fraud detection is becoming more important in environments where data patterns change over time. This makes it necessary to adjust detection systems to keep up with new fraud strategies. The main motivation of this study is to replicate and extend the study of \cite{dal2015credit}, which proposed novel approaches to address concept drift in fraud detection within real-world settings. The original study used proprietary datasets. We conducted the replication study using two open access datasets. We extended the replication study by a) comparing the algorithms in the original study with an algorithm used in another study; and b) with algorithms that do not account for concept drift. Some of our results align with the original study, while others differ. For example, we found consistent results with the original study that treating delayed feedbacks separately from immediate feedback leads to higher recall rates in fraud detection. On the other hand, we found that some simple methods for dealing with concept drift may give the same or even better results than more complex methods.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".