Ihsan Chaoubi, Camille Besse, Hélène Cossette, Marie-Pier Côté
arXiv 27 Jan 2022 · Machine Learning · publishedApplied Stochastic Models in Business and Industry (2023) · 7 citations (OpenAlex)
arXiv:2201.13267 · PDF · DOI · OpenAlex · Extracted main text
Detailed information about individual claims are completely ignored when insurance claims data are aggregated and structured in development triangles for loss reserving. In the hope of extracting predictive power from the individual claims characteristics, researchers have recently proposed to move away from these macro-level methods in favor of micro-level loss reserving approaches. We introduce a discrete-time individual reserving framework incorporating granular information in a deep learning approach named Long Short-Term Memory (LSTM) neural network. At each time period, the network has two tasks: first, classifying whether there is a payment or a recovery, and second, predicting the corresponding non-zero amount, if any. We illustrate the estimation procedure on a simulated and a real general insurance dataset. We compare our approach with the chain-ladder aggregate method using the predictive outstanding loss estimates and their actual values. Based on a generalized Pareto model for excess payments over a threshold, we adjust the LSTM reserve prediction to account for extreme payments.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Delong ukasz, Wüthrich Mario V. Neural networks for the joint develo… (2020) ;8:33 | 0.811 | 4 | 2 | 100% |
| 2 | Gabrielli Andrea. An individual claims reserving model for reported… (2021) ;DOI:10.1007/s13385-021-00271-4 | 0.737 | 3 | 2 | 100% |
| 3 | Gabrielli Andrea, Wüthrich Mario. An individual claims history simul… (2018) ;6:29 | 0.511 | 2 | 2 | 50% |
| 4 | Delong ukasz, Lindholm Mathias, Wüthrich Mario V. Collective reservi… (2021) ;DOI: 10.1080/03461238.2021.1921836 | 0.511 | 2 | 1 | 100% |
| 5 | Kendall Alex, Gal Yarin, Cipolla Roberto. Multi-task learning using… (2018) | 0.511 | 2 | 1 | 100% |
| 6 | Kuo Kevin. Individual claims forecasting with Bayesian mixture densi… (2003) 02453 | 0.511 | 2 | 1 | 100% |
| 7 | Wüthrich Mario V. Machine learning in individual claims reserving Sc… (2018) 1–16 | 0.511 | 2 | 1 | 100% |
| 8 | Antonio Katrien, Plat Richard. Micro-level stochastic loss reserving… (2014) ;2014:649–669 | 0.405 | 1 | 1 | 100% |
| 9 | Arjas Elja. The claims reserving problem in non-life insurance: Some… (1989) ;19:139–152 | 0.405 | 1 | 1 | 100% |
| 10 | Baudry Maximilien, Robert Christian Y. A machine learning approach f… (2019) ;35:1127–1155 | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 38 scored citations.