A machine learning approach for individual claims reserving in insurance
Problem
Insurance companies must accurately estimate outstanding claim liabilities, traditionally done with aggregate run-off triangle models rather than individual claims data.
Why it matters
Loss reserves are a key financial statement item, and misestimation affects an insurer's solvency and pricing.
What the AI did
The authors develop a nonparametric machine learning method trained on individual claim histories and policy covariates, handling right-censored occurrence and severity targets.
Results
not stated in source
Impact
The paper proposes and evaluates an ML-based individual claims reserving approach, but the abstract truncates before the comparison results.
Record
| Venue | Applied Stochastic Models in Business and Industry |
|---|---|
| Published | 2019 |
| Work type | article |
| Authors | Maximilien Baudry, Christian Y. Robert |
| Citations | 60 (OpenAlex, at import time) |
| References | 14 |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1002/asmb.2455 |
| Open access | closed |
| Keywords | machine learning, claims reserving, insurance, nonparametric estimation |
Importance rating
real but narrower advance
Loss reserves are a key insurer financial statement item affecting solvency and pricing; abstract gives no quantified scale or results.
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