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Macro vs. micro methods in non-life claims reserving (an econometric perspective)

Charpentier, Arthur; Pigeon, Mathieu - In: Risks 4 (2016) 2, pp. 1-18

Traditionally, actuaries have used run-off triangles to estimate reserve ("macro" models, on aggregated data). However, it is possible to model payments related to individual claims. If those models provide similar estimations, we investigate uncertainty related to reserves with "macro" and...

Persistent link: https://www.econbiz.de/10011709553

An individual claims history simulation machine

Gabrielli, Andrea; Wüthrich, Mario V. - In: Risks 6 (2018) 2, pp. 1-32

The aim of this project is to develop a stochastic simulation machine that generates individual claims histories of non-life insurance claims. This simulation machine is based on neural networks to incorporate individual claims feature information. We provide a fully calibrated stochastic...

Persistent link: https://www.econbiz.de/10011996587

Individual loss reserving using a gradient boosting-based approach

Duval, Francis; Pigeon, Mathieu - In: Risks 7 (2019) 3, pp. 1-18

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. These claim-level models use information about each of the payments made for each of the claims in...

Persistent link: https://www.econbiz.de/10013200497

Loss reserving models: Granular and machine learning forms

Taylor, Greg - In: Risks 7 (2019) 3, pp. 1-18

The purpose of this paper is to survey recent developments in granular models and machine learning models for loss reserving, and to compare the two families with a view to assessment of their potential for future development. This is best understood against the context of the evolution of these...

Persistent link: https://www.econbiz.de/10013200500

Penalising unexplainability in neural networks for predicting payments per claim incurred

H. L. Poon, Jacky - In: Risks 7 (2019) 3, pp. 1-11

In actuarial modelling of risk pricing and loss reserving in general insurance, also known as P&C or non-life insurance, there is business value in the predictive power and automation through machine learning. However, interpretability can be critical, especially in explaining to key...

Persistent link: https://www.econbiz.de/10013200513

Deeptriangle: A deep learning approach to loss reserving

Kuo, Kevin - In: Risks 7 (2019) 3, pp. 1-12

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. We validate the models on loss reserving data across lines of business, and show that they...

Persistent link: https://www.econbiz.de/10013200515

Loss reserving estimation with correlated run-off triangles in a quantile longitudinal model

Badounas, Ioannis; Pitselis, Georgios - In: Risks 8 (2020) 1, pp. 1-26

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. The model proposes a combination of the between- and within-subportfolios...

Persistent link: https://www.econbiz.de/10013200549

Defining geographical rating territories in auto insurance regulation by spatially constrained clustering

Xie, Shengkun; Esposito, Emilio Xavier - In: Risks 7 (2019) 2, pp. 1-20

equity of pure premium, as required by insurance regulation. To achieve this goal, the spatially-constrained clustering of … the design of geographical rating territories, a clustering approach based on Delaunay triangulation is proposed …-constrained clustering approach in defining geographical rating territories for insurance rate regulation purposes. The significance of this …

Persistent link: https://www.econbiz.de/10013200460

Regularization of autoencoders for bank client profiling based on financial transactions

Filchenkov, Andrey; Khanzhina, Natalia; Tsai, Arina; … - In: Risks 9 (2021) 3, pp. 1-16

Predicting if a client is worth giving a loan-credit scoring-is one of the most essential and popular problems in banking. Predictive models for this goal are built on the assumption that there is a dependency between the client's profile before the loan approval and their future behavior....

Persistent link: https://www.econbiz.de/10013200723

Concordance probability for insurance pricing models

Ponnet, Jolien; Van Oirbeek, Robin; Verdonck, Tim - In: Risks 9 (2021) 10, pp. 1-26

The concordance probability, also called the C-index, is a popular measure to capture the discriminatory ability of a predictive model. In this article, the definition of this measure is adapted to the specific needs of the frequency and severity model, typically used during the technical...

Persistent link: https://www.econbiz.de/10013200842