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Loss reserving models : granular and machine learning forms

Taylor, Greg - In: Risks : open access journal 7 (2019) 3/82, 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/10012127545

Deeptriangle : a deep learning approach to loss reserving

Kuo, Kevin - In: Risks : open access journal 7 (2019) 3/97, 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/10012126426

Advancing the use of deep learning in loss reserving : a Generalized DeepTriangle approach

Feng, Yining; Li, Shuanming - In: Risks : open access journal 12 (2024) 1, pp. 1-14

This paper proposes a generalized deep learning approach for predicting claims developments for non-life insurance reserving. The generalized approach offers more flexibility and accuracy in solving actuarial reserving problems. It predicts claims outstanding weighted by exposure instead of loss...

Persistent link: https://www.econbiz.de/10014480914

AutoReserve: a web-based tool for personal auto insurance loss reserving with classical and machine learning methods

Xiong, Lu; Manathunga, Vajira; Luo, Jiyao; Dennison, … - In: Risks : open access journal 11 (2023) 7, pp. 1-17

In this paper, we developed a Shiny-based application called AutoReserve. This application serves as a tool used for a variety of types of loss reserving. The primary target audience of the app is personal auto actuaries, who are professionals in the insurance industry specializing in assessing...

Persistent link: https://www.econbiz.de/10014391584

A neural feature extraction model for classification of firms and prediction of outsourcing success : advantage of using relational sources of information for new suppliers

Medhi, Pankaj Kumar; Mondal, Sandeep - In: International journal of production research 54 (2016) 19/20, pp. 6071-6081

Persistent link: https://www.econbiz.de/10011543743

Thai stock news classification based on price changes and sentiments

Ponrudee Netisopakul; Woranun Saewong - In: International journal of electronic finance : IJEF 11 (2022) 1, pp. 49-66

Persistent link: https://www.econbiz.de/10012939852

Analysis of the cryptocurrency market using different prototype-based clustering techniques

Lorenzo, Luis; Arroyo Gallardo, Javier - In: Financial innovation : FIN 8 (2022), pp. 1-46

entire cryptocurrency market in 2018, using data analysis tools. Accordingly, we propose a new clustering-based methodology … between the clustering results, and other factors that are not involved in clustering. Particularly, the methodology involves … applying three different partitional clustering algorithms, where each of them use a different representation for …

Persistent link: https://www.econbiz.de/10012799125

Cyber claim analysis using Generalized Pareto regression trees with applications to insurance

Farkas, Sébastien; Lopez, Olivier; Thomas, Maud - In: Insurance / Mathematics & economics 98 (2021), pp. 92-105

Persistent link: https://www.econbiz.de/10012545270

A general framework of derivatives pricing

Zhang, Liangliang - In: Journal of mathematical finance 10 (2020) 2, pp. 255-266

Persistent link: https://www.econbiz.de/10012545688

An integrated approach to renew software contract using machine learning.

John, Shylu; Shah, Bhavin; Dixit, Varun; Wani, Amol - In: Journal of business analytics 4 (2021) 1, pp. 14-25

Persistent link: https://www.econbiz.de/10012513658