IMPLEMENTATION OF FUZZY C-MEANS ALGORITHM AND FUZZY INFERENCE SYSTEM ON INSURANCE DATA

The cash inflow of an insurance company is mainly coming from the premiums paid. In general, the premiums set by the company are higher than the premiums calculated by the equivalence principle. As the main source of cash, premiums need to be well-managed to increase the company’s income from in...

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Main Author: ANTHONY
Format: Final Project
Language:Indonesia
Online Access:https://digilib.itb.ac.id/gdl/view/65109
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Institution: Institut Teknologi Bandung
Language: Indonesia
id id-itb.:65109
spelling id-itb.:651092022-06-20T18:38:55ZIMPLEMENTATION OF FUZZY C-MEANS ALGORITHM AND FUZZY INFERENCE SYSTEM ON INSURANCE DATA ANTHONY Indonesia Final Project Insurance, fuzzy c-means algorithm, fuzzy inference system, reserve INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/65109 The cash inflow of an insurance company is mainly coming from the premiums paid. In general, the premiums set by the company are higher than the premiums calculated by the equivalence principle. As the main source of cash, premiums need to be well-managed to increase the company’s income from investments while still be able to cover future risks. However, risk related information from secondary data are commonly incomplete. Therefore, the fuzzy concept is considered to analyze the incomplete information. The fuzzy concept is a logical concept with a vague (fuzzy) truth value, hence able to describe incomplete information. The fuzzy concepts used in this study consists of the fuzzy c-means algorithm as a data grouping method and the fuzzy inference system as a logic gate in taking decisions. The policyholders’ data are grouped by age into four categories, which are young, young adult, adult, and old. While based on premium price level (from the policy’s description), data is grouped into four groups, which are cheap, fair, expensive, and extremely expensive. Both variables are used as an input to the fuzzy inference system to create an output of percentage premium to save. Percentage premium to save is split into six categories, which are minimum, low, moderate, moderate high, high, and maximum. The fraction of the received premium can then be calculated by using the created fuzzy inference system. The reserved premium is then accumulated, creating the company’s reserves, while the rest of the fund is invested accordingly. Reserves are then compared to the sum of valid policy values, that is the difference of expected present value of benefits and premiums. The accumulated reserve only covered a fraction of the valid policy value. This means the company will not be able to cover future risks of the valid policies. Therefore, the company needs to allocate a part of its investment funds to the reserves periodically so that the reserve can cover the valid policy value. 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 The cash inflow of an insurance company is mainly coming from the premiums paid. In general, the premiums set by the company are higher than the premiums calculated by the equivalence principle. As the main source of cash, premiums need to be well-managed to increase the company’s income from investments while still be able to cover future risks. However, risk related information from secondary data are commonly incomplete. Therefore, the fuzzy concept is considered to analyze the incomplete information. The fuzzy concept is a logical concept with a vague (fuzzy) truth value, hence able to describe incomplete information. The fuzzy concepts used in this study consists of the fuzzy c-means algorithm as a data grouping method and the fuzzy inference system as a logic gate in taking decisions. The policyholders’ data are grouped by age into four categories, which are young, young adult, adult, and old. While based on premium price level (from the policy’s description), data is grouped into four groups, which are cheap, fair, expensive, and extremely expensive. Both variables are used as an input to the fuzzy inference system to create an output of percentage premium to save. Percentage premium to save is split into six categories, which are minimum, low, moderate, moderate high, high, and maximum. The fraction of the received premium can then be calculated by using the created fuzzy inference system. The reserved premium is then accumulated, creating the company’s reserves, while the rest of the fund is invested accordingly. Reserves are then compared to the sum of valid policy values, that is the difference of expected present value of benefits and premiums. The accumulated reserve only covered a fraction of the valid policy value. This means the company will not be able to cover future risks of the valid policies. Therefore, the company needs to allocate a part of its investment funds to the reserves periodically so that the reserve can cover the valid policy value.
format Final Project
author ANTHONY
spellingShingle ANTHONY
IMPLEMENTATION OF FUZZY C-MEANS ALGORITHM AND FUZZY INFERENCE SYSTEM ON INSURANCE DATA
author_facet ANTHONY
author_sort ANTHONY
title IMPLEMENTATION OF FUZZY C-MEANS ALGORITHM AND FUZZY INFERENCE SYSTEM ON INSURANCE DATA
title_short IMPLEMENTATION OF FUZZY C-MEANS ALGORITHM AND FUZZY INFERENCE SYSTEM ON INSURANCE DATA
title_full IMPLEMENTATION OF FUZZY C-MEANS ALGORITHM AND FUZZY INFERENCE SYSTEM ON INSURANCE DATA
title_fullStr IMPLEMENTATION OF FUZZY C-MEANS ALGORITHM AND FUZZY INFERENCE SYSTEM ON INSURANCE DATA
title_full_unstemmed IMPLEMENTATION OF FUZZY C-MEANS ALGORITHM AND FUZZY INFERENCE SYSTEM ON INSURANCE DATA
title_sort implementation of fuzzy c-means algorithm and fuzzy inference system on insurance data
url https://digilib.itb.ac.id/gdl/view/65109
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