Literature Review

Collect published research on Machine Learning in reserving techniques
Published

September 30, 2021

The Literature Review Workstream collects published research on Machine Learning (ML) in reserving techniques. The literature collected so far discuss the following methods: Neural Networks (NN), Gradient Boosting Machines (GBM) and random forests (RF). By summer 2020, the workstream reviewed a total of 69 papers and continues to scan for new relevant papers to enrich the lessons learned.

There is a growing body of literature on application of ML techniques to general insurance reserving. The majority use individual claims data, and this possibly could be extended to cover micro-level data points in the region surrounding the claim event to include: transaction dates, reopened claims, closing dates etc. There is still clearly a lot to learn and a compelling case for use of simulated data to close gaps in data is clearly present and should be considered.

Reviewed Papers

A compilation of the full list of papers reviewed by the working party as at summer 2020 can be found here. We are continuing to review papers and will update this page from time to time.

Papers of Interest

We have flagged papers of particular interest in the table below.

Title Model Notes
Ahlgren M (2018). Claims reserving using gradient boosting and Generalized Linear Models GBM Compares GBMs with GLMs. Conclusion is that the performance of the two is similar.GLM is then preferred on the ground of interpretability.
Duval F and Pigeon M (2019). Individual loss reserving using a gradient boosting-based approach GBM Uses GBM to boost structured GLM micro-models.
Taylor, G. (2019). Loss reserving models: Granular and machine learning forms NN Surveys recent developments in granular models and machine learning models for loss reserving, and assesses their potential for future development.
Gabrielli, A., Richman, R., and Wüthrich, M. V. (2020). Neural network embedding of the over-dispersed Poisson reserving model NN This paper embeds a classical actuarial regression model into a neural network architecture. Such models can be fitted efficiently which allows us to explore bootstrap methods for prediction uncertainty.
Kuo, K. (2020). Individual claims forecasting with Bayesian mixture density networks NN We introduce an individual claims forecasting framework utilizing Bayesian mixture density networks that can be used for claims analytics tasks such as case reserving and triaging.
Baudry M and Robert C (2017). Non-parametric individual claim reserving in insurance RF Model results in reasonable forecasts with a prediction error much reduced relative to Chain-Ladder.
De Felice M and Moriconi F (2019). Claim Watching and Individual Claims Reserving Using Classification and Regression Trees RF This model forecasts not only the ultimate claim cost but also the claim status at various points in time.

Emerging Literature Gaps

The papers reviewed showed us some gaps in the literature that need to be followed up. For example, model classifications of interest to the working party are:

  • Bayesian Network Classifiers
  • Ensemble models
  • Mixed Density Function

We will be collecting papers for the above models and reviewing them in the coming months so that summaries and learnings can be published.

Posts

Date Title Author
Sep 30, 2021 Probabilistic Forecasting with Neural Networks Applied to Loss Reserving Sarah MacDonnell
Jul 26, 2021 A brief history of papers looking at using neural networks in reserving Kevin Kuo
Feb 25, 2021 Introducing our Literature Review home page Tawanda Chituku, Caroline Yeomans
Nov 30, 2020 Reserving using machine learning - an advanced example in R (Part 3) Nigel Carpenter
Nov 23, 2020 Reserving using machine learning - an advanced example in R (Part 2) Nigel Carpenter
Nov 13, 2020 Reserving using machine learning - an advanced example in R (Part 1) Nigel Carpenter
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Talks

Date Title Speakers
Nov 2, 2020 Machine Learning in Reserving - IFoA Working Group Sarah MacDonnell, Jacque Friedland, Grainne McGuire, Nigel Carpenter, Kevin Kuo
Sep 30, 2020 AI in Reserving Sarah MacDonnell, Jacque Friedland, Grainne McGuire, Nigel Carpenter, Kevin Kuo
Sep 15, 2020 Machine Learning in Reserving Sarah MacDonnell, Jacque Friedland, Grainne McGuire, Nigel Carpenter, Kevin Kuo
Mar 12, 2020 Machine learning in GI reserving Nigel Carpenter
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