Cost-Sensitive Learning for Medical Insurance Fraud Detection With Temporal Information
Bibliographic record
Abstract
Fraudulent activities within the U.S. healthcare system cost billions of dollars each year and harm the wellbeing of many qualifying beneficiaries. The implementation of an effective fraud detection method has become imperative to secure the welfare of the general public. In this article, we focus on the problem of fraud detection using the current year's Medicare claims data from the perspective of utilizing temporal information from the previous years. We group the data into temporal trajectories of the key covariates and base our feature engineering around these trajectories. For effective feature engineering on the temporal data, we propose to use the functional principal component analysis (FPCA) method for analyzing the temporal covariates’ trajectory as well as the distributional FPCA for extracting features from the empirical probability density curve of the covariates. Moreover, we introduce the framework of cost-sensitive learning for analyzing the Medicare database to allow for asymmetrical losses in the confusion matrix, such that the classification rule reflects the realistic tradeoff between the fixed cost and the fraud cost. The issue of class imbalance in the database is tackled through the random undersampling scheme. Our results confirm that the trained classifier has a reasonably good prediction performance and a significant percentage of cost savings can be achieved by taking into account the financial cost.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".