Higher-Order Feature Synthesis for Insurance Scoring Models
Bibliographic record
Abstract
In many jurisdictions, automobile insurers have access to risk-sharing pools to which they can transfer some of their worst risks. Better selection of these risks in order to maximize profitability is the application we consider in this paper. To that end, different feature selection and modeling approaches are tested and compared against the historical data of a mid-sized Canadian insurer. In particular, we introduce a flexible scoring model that estimates each risk’s loss ratio, a target that is better suited to the application considered than the usual claims level. We also devise a feature selection method that is robust to overfitting due to the use of a rank averaging technique. By analogy to the knapsack problem, we show what should be the most suitable sorting criterion depending on pool regulations. Given the sequential structure of insurance data, model selection through cross validation with free permutation of instances must be discarded. Instead, we use a similar technique but that is coherent with the sequential structure. We explain how different software maturity levels lead to different levels of information being available at the time a decision must be made which in turn, impacts profitability.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".