Kalman Filter Learning Algorithms and State Space Representations for Stochastic Claims Reserving
Risks, 2021 In stochastic claims reserving, state space models have been used for almost 40 years to forecast loss reserves and to compute their mean squared error of prediction.
Nataliya Chukhrova, Arne Johannssen
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Rank-Based Multivariate Sarmanov for Modeling Dependence between Loss Reserves
Risks, 2023 The interdependence between multiple lines of business has an important impact on determining loss reserves and risk capital, which are crucial for the solvency of a property and casualty (P&C) insurance company.
Anas Abdallah, Lan Wang
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Advantages and disadvantages of loss reserving methods in non-life insurance [PDF]
Yugoslav Journal of Operations Research, 2019 We analyse characteristics of the three most commonly used methods for estimating loss reserves in non life insurance: the chain ladder method, the loss ratio method, and the Bornhuetter-Ferguson method.
Kočović Jelena +2 more
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Micro-Level Stochastic Loss Reserving [PDF]
SSRN Electronic Journal, 2010 To meet future liabilities general insurance companies will set-up reserves. Predicting future cash-flows is essential in this process. Actuarial loss reserving methods will help them to do this in a sound way. The last decennium a vast literature about stochastic loss reserving for the general insurance business has been developed.
Antonio, K., Plat, R.
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Confidence bounds for discounted loss reserves [PDF]
Insurance: Mathematics and Economics, 2003 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Hoedemakers, Tom +3 more
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Loss Reserving Estimation With Correlated Run-Off Triangles in a Quantile Longitudinal Model
Risks, 2020 In this paper, we consider a loss reserving model for a general insurance portfolio consisting of a number of correlated run-off triangles that can be embedded within the quantile regression model for longitudinal data.
Ioannis Badounas, Georgios Pitselis
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Model Error (or Ambiguity) and Its Estimation, with Particular Application to Loss Reserving
Risks, 2023 This paper is concerned with the estimation of forecast error, particularly in relation to insurance loss reserving. Forecast error is generally regarded as consisting of three components, namely parameter, process and model errors.
Greg Taylor, Gráinne McGuire
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Predicting loss reserves using quantile regression Running title: Quantile regression loss reserve models [PDF]
Journal of Data Science, 2021 Traditional loss reserves models focus on the mean of the conditional loss distribution. If the factors driving high claims differ systematically from those driving medium to low claims, alternative models that differentiate such differences are required.
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DeepTriangle: A Deep Learning Approach to Loss Reserving
Risks, 2019 We propose a novel approach for loss reserving based on deep neural networks. The approach allows for joint modeling of paid losses and claims outstanding, and incorporation of heterogeneous inputs.
Kevin Kuo
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Individual Loss Reserving Using a Gradient Boosting-Based Approach
Risks, 2019 In this paper, we propose models for non-life loss reserving combining traditional approaches such as Mack’s or generalized linear models and gradient boosting algorithm in an individual framework.
Francis Duval, Mathieu Pigeon
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