APPLICATION OF SCALED CONJUGATE GRADIENT ARTIFICIAL NEURAL NETWORK ON CHAIN-LADDER RESERVING METHOD

APPLICATION OF SCALED CONJUGATE GRADIENT ARTIFICIAL NEURAL NETWORK ON CHAIN-LADDER RESERVING METHOD

With the rapid and increasing development in information technology, the explosion of the amount of data, called Big Data, is occurring. This phenomenon also affects the insurance industry, where the characteristics of each claim data could be obtained and recorded faster and better in terms of its...

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Main Author: FILBERT ANDARIAS (NIM: 10114061), NIKOLAS
Format: Final Project
Language:Indonesia
Online Access:https://digilib.itb.ac.id/gdl/view/29633
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Institution: Institut Teknologi Bandung
Language: Indonesia
id id-itb.:29633
spelling id-itb.:296332018-06-28T13:33:03ZAPPLICATION OF SCALED CONJUGATE GRADIENT ARTIFICIAL NEURAL NETWORK ON CHAIN-LADDER RESERVING METHOD FILBERT ANDARIAS (NIM: 10114061), NIKOLAS Indonesia Final Project INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/29633 With the rapid and increasing development in information technology, the explosion of the amount of data, called Big Data, is occurring. This phenomenon also affects the insurance industry, where the characteristics of each claim data could be obtained and recorded faster and better in terms of its quantity and quality. Hence, the information in the data could be used to increase the accuracy of the estimate of the outstanding claims liability. In this final project, the Chain-Ladder reserving method with Scaled Conjugate Gradient Artificial Neural Network is discussed. The method is then applied to an Indonesian earthquake insurance claim data to determine the outstanding claims liability. text
institution Institut Teknologi Bandung
building Institut Teknologi Bandung Library
continent Asia
country Indonesia
Indonesia
content_provider Institut Teknologi Bandung
collection Digital ITB
language Indonesia
description With the rapid and increasing development in information technology, the explosion of the amount of data, called Big Data, is occurring. This phenomenon also affects the insurance industry, where the characteristics of each claim data could be obtained and recorded faster and better in terms of its quantity and quality. Hence, the information in the data could be used to increase the accuracy of the estimate of the outstanding claims liability. In this final project, the Chain-Ladder reserving method with Scaled Conjugate Gradient Artificial Neural Network is discussed. The method is then applied to an Indonesian earthquake insurance claim data to determine the outstanding claims liability.
format Final Project
author FILBERT ANDARIAS (NIM: 10114061), NIKOLAS
spellingShingle FILBERT ANDARIAS (NIM: 10114061), NIKOLAS
APPLICATION OF SCALED CONJUGATE GRADIENT ARTIFICIAL NEURAL NETWORK ON CHAIN-LADDER RESERVING METHOD
author_facet FILBERT ANDARIAS (NIM: 10114061), NIKOLAS
author_sort FILBERT ANDARIAS (NIM: 10114061), NIKOLAS
title APPLICATION OF SCALED CONJUGATE GRADIENT ARTIFICIAL NEURAL NETWORK ON CHAIN-LADDER RESERVING METHOD
title_short APPLICATION OF SCALED CONJUGATE GRADIENT ARTIFICIAL NEURAL NETWORK ON CHAIN-LADDER RESERVING METHOD
title_full APPLICATION OF SCALED CONJUGATE GRADIENT ARTIFICIAL NEURAL NETWORK ON CHAIN-LADDER RESERVING METHOD
title_fullStr APPLICATION OF SCALED CONJUGATE GRADIENT ARTIFICIAL NEURAL NETWORK ON CHAIN-LADDER RESERVING METHOD
title_full_unstemmed APPLICATION OF SCALED CONJUGATE GRADIENT ARTIFICIAL NEURAL NETWORK ON CHAIN-LADDER RESERVING METHOD
title_sort application of scaled conjugate gradient artificial neural network on chain-ladder reserving method
url https://digilib.itb.ac.id/gdl/view/29633
_version_ 1822022140571942912

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