Machine Learning Techniques in Usage-Based Insurance: \nUse of Telematic Data in Auto Insurance
Bibliographic record
Abstract
The development of big data technologies and in-vehicle devices has contributed to the growth of Usage-Based Insurance (UBI) in recent years. These in-vehicle devices, such as GPS and sensors, collect certain variables that can represent the driving behaviour of policyholders. This collected data, called telematic data, consist of several variables that have strong relationship with likelihood of having an accident. Consequently, one can use telematic data to improve risk assessment and personalize car insurance premiums. In this thesis, a synthetic car insurance dataset emulated from a Canadian-based insurance company is used to investigate the use of telematic data in predicting the likelihood of having an accident. More precisely four machine learning techniques—logistic regression, random forests, gradient boosting trees, and feed-forward neural networks—are employed to predict the risk of having an accident. Actuaries often use white box machine learning methods like logistic regression for risk assessment due to their interpretability. However, these method are unable to detect non-linear relationships between variables accurately. Therefore, more \ncomplex machine learning techniques such as random forests, gradient boosting trees, and feed-forward neural networks are used to achieve more accurate risk assessment for accidents. \nIn addition, two variable importance assessment methods—Shapley decomposition and marginal performance loss upon feature removal—are employed to provide insights into the \nfeature contributions in the overall predictive performance of the models.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".