Auto insurance fraud detection using unsupervised spectral ranking for anomaly
Bibliographic record
Abstract
For many data mining problems, obtaining labels is costly and time consuming, if not practically infeasible. In addition, unlabeled data often includes categorical or ordinal features which, compared with numerical features, can present additional challenges. We propose a new unsupervised spectral ranking method for anomaly (SRA). We illustrate that the spectral optimization in SRA can be viewed as a relaxation of an unsupervised SVM problem. We demonstrate that the first non-principal eigenvector of a Laplacian matrix is linked to a bi-class classification strength measure which can be used to rank anomalies. Using the first non-principal eigenvector of the Laplacian matrix directly, the proposed SRA generates an anomaly ranking either with respect to the majority class or with respect to two main patterns. The choice of the ranking reference can be made based on whether the cardinality of the smaller class (positive or negative) is sufficiently large. Using an auto insurance claim data set but ignoring labels when generating ranking, we show that our proposed SRA significantly surpasses existing outlier-based fraud detection methods. Finally we demonstrate that, while proposed SRA yields good performance for a few similarity measures for the auto insurance claim data, notably ones based on the Hamming distance, choosing appropriate similarity measures for a fraud detection problem remains crucial.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".