Machine Learning Based Method for Insurance Fraud Detection on Class Imbalance Datasets With Missing Values
Bibliographic record
Abstract
Insurance fraud is a prevalent issue that insurance companies must face, particularly in the realm of automobile insurance. This type of fraud has significant cost implications for insurance firms and can have a long-term impact on pricing strategies and insurance rates. As a result, accurately predicting and detecting insurance fraud has become a crucial challenge for insurers. The fraud datasets are usually imbalanced, as the number of fraudulent instances is much less than the ligament instances and contains missing values. Prior research has employed machine learning methods to address this class imbalance dataset problem, but there is limited effort handling the class imbalance dataset present in insurance fraud datasets. Moreover, we could not find an overfitting analysis for the relevant predictive models. This paper addresses these two limitations by employing two car insurance company datasets, namely, an Egyptian real-life dataset and a standard dataset. We proposed addressing the missing data and the class imbalance problems with different methods. Then, the predictive models were trained on processed datasets to predict insurance fraud as a classification problem. The classifiers are evaluated on several evaluation metrics. Moreover, we proposed the first overfitting analysis for insurance fraud classifiers, to our knowledge. The obtained results outline that addressing the class imbalance in the insurance fraud detection dataset has a significant positive effect on the performance of the predictive model, while addressing the problem of missing values has a slight effect. Moreover, the proposed methods outperform all of the existing methods on the accuracy metric.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".