Unsupervised Financial Fraud Detection Using Low-rank Recovery
Bibliographic record
Abstract
Many outlier detection methods have been studied and applied to financial fraud detection problems in recent years. These traditional financial fraud detection problems (such as classification-based methods) often suffer performance degradation caused by imbalanced data. Even worse, the frauds often destroy the low dimensional structure of the sub-space on which the intrinsic samples are distributed, and the traditional financial fraud detection methods fail to study the low rankness prior in the intrinsic samples when outliers emerge. We give an effective unsupervised financial fraud detection algorithm based on a low-rank recovery method to solve these issues. Our work has several folds: (1) We adopt the Outlier Pursuit (OP) algorithm and its non-convex variants, which can study the low rankness within the intrinsic samples and detect the outliers without supervision. (2) We solve OP and its non-convex variants by combining Alternating Direction Method Of Multipliers (ADMM) with Generalized Accelerating Iterative Algorithm (GAI). (3) We evaluate the effectiveness of the OP-based methods and compare methods on three data sets, including the German Credit Data, Insurance Company (TIC) Benchmark, and IEEE-CIS fraud Detection data set. The related code is available at https://github.com/jzheng20/FinancialFraudDetectionViaOP.git.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".