scholarly journals Clustering Customer Data Using Fuzzy C-Means Algorithm

2021 ◽  
Vol 9 (1) ◽  
pp. 1-14
Author(s):  
Nurfaizah Nurfaizah ◽  
Fathuzaen Fathuzaen
Keyword(s):  
Economic Growth ◽  
Partition Coefficient ◽  
Economic Activity ◽  
Service Industry ◽  
Insurance Companies ◽  
Insurance Company ◽  
Validity Index ◽  
Customer Data ◽  
Fuzzy C Means ◽  
Fuzzy C Means Algorithm

The pattern of the service industry is influenced mostly by economic growth. When economic growth rises, the economic activity will also grow as in the case of insurance activities. One of the assets owned by an insurance company is the customer, hence the existence of a loyal or potential customer should be maintained by the insurance company. This study focuses on clustering or grouping the existing customer data in insurance companies using the Fuzzy C-Means (FCM) algorithm. This study uses data from the company for analysis and the results can be used as a basis for insurance companies in making decisions, especially those related to further insurance marketing to customers who have participated in insurance or who are still actively registered in payment insurance. Fuzzy C-Means can be used for clustering the customer datasets. It obtained 3 clustering results using Partition Coefficient (PC) in determining the validity index and the centers value was ranged from 0.5 to 1.0.  

scholarly journals Measuring the effectiveness of intermediary loyalty programmes in the motor insurance industry: loyal versus non-loyal customers

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Manuel Leiria ◽  
Efigénio Rebelo ◽  
Nelson deMatos
Keyword(s):  
Insurance Industry ◽  
Binary Logistic Regression ◽  
Data Access ◽  
Insurance Companies ◽  
Insurance Company ◽  
Content Type ◽  
Customer Data ◽  
Insurance Policies ◽  
Motor Insurance ◽  
Value Propositions

PurposeThe insurance industry has not been able to effectively retain its customers and struggles to establish and maintain long-lasting relationships with them. The purpose of this paper is thus to identify the main factors that explain the cancellation of motor insurance policies by individual customers, considering the influence of intermediaries on their decisions.Design/methodology/approachThe data used in this research is based on a sample of 3,500 insurance policies that lapsed during the period of analysis between January and July 2017, against another sample of 3,500 policies that did not lapse, from a major insurance company in Portugal. Binary logistic regression was used for data analysis, using IBM SPSS software.FindingsAggressive tactics by insurance companies for customer acquisition may induce the cancellation of insurance policies. More valuable customers, the policies with higher premiums and recent claims, as well as the ancillary intermediaries and agents, are determinants of insurance cancellation. Conversely, the payment of policies by direct debit and without instalments reduces the probability of cancellations.Research limitations/implicationsThe main limitation of this study is the restriction on data access. Insurance companies are significantly resistant to sharing their customer data – including with academic researchers – even in an anonymised form.Practical implicationsThe paper highlights internal and external practices of insurance companies that should be reformulated to significantly improve their performance regarding product cancellation, related to customer information management, mistrust behaviours related to stakeholders and new value propositions that deepen the relationships with intermediaries.Originality/valueThis research developed a framework with which to identify the factors that are mainly associated with motor insurance cancellation and to predict its likelihood.

scholarly journals Application of C4.5 Algorithm for Late Payment Classification of Insurance Premiums

2021 ◽  
Vol 9 (2) ◽  
pp. 100-113
Author(s):  
Jefry Antonius Karlia ◽  
Wawan Nurmansyah
Keyword(s):  
Insurance Companies ◽  
Insurance Company ◽  
Test Results ◽  
Customer Data ◽  
Insurance Premiums ◽  
Premium Payment ◽  
C4.5 Algorithm ◽  
The Status ◽  
Number Of Customers

The problem that often arises in insurance companies is the number of customers who do not smoothly pay premiums. The procedure that applies to the insurance during the grace period is 30 days. The insured customer must follow the premium payment procedure, if the customer does not pay the premium, the insurance policy will be canceled, this is part of the company's loss. An insurance company has a lot of data and this data can be processed to produce information on how to find out potential customer delays from a pattern formed using the C4.5 method. This research was conducted by applying the C4.5 algorithm using insurance customer data. The results of this study are a classification system for late payment of insurance premiums that can classify insurance customer premium payment status as a consideration for accepting insurance customers. The system test results show that the system can classify the status of insurance customer premium payments with a classification accuracy of 88%. Keywords: Algorithm C 4.5, Insurance, Classification, Premium

A novel cluster validity index for fuzzy C-means algorithm

2016 ◽  
Vol 22 (6) ◽  
pp. 1921-1931 ◽  
Author(s):  
Shuling Yang ◽  
Kangshun Li ◽  
Zhengping Liang ◽  
Wei Li ◽  
Yu Xue
Keyword(s):  
Cluster Validity ◽  
Cluster Validity Index ◽  
Validity Index ◽  
Fuzzy C Means ◽  
Fuzzy C Means Algorithm

scholarly journals Penerapan Data Mining untuk Memprediksi Minat Nasabah Terhadap Produk Asuransi Meninggal Dunia dengan Metode Naïve Bayes (Studi Kasus : PT. BNI Life Insurance)

Respati ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. 103
Author(s):  
Ari Hidayatullah, Ena Mudiawati, Muhammad Syafrullah
Keyword(s):  
Data Mining ◽  
Life Insurance ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Insurance Companies ◽  
Insurance Company ◽  
Accuracy Rate ◽  
Customer Data ◽  
Bayes Algorithm ◽  
Business Behavior

INTISASIPendapatan untuk perusahaan asuransi ditentukan oleh jumlah premi yang dibayar oleh nasabah. faktor penting nasabah berupa premi, premi ditentukan dalam persentase atau tarif tertentu. Pada perusahaan asuransi pasti memiliki jumlah data, dan data tersebut sangat penting bagi perusahaan untuk mengetahui kriteria nasabah yang berminat pada asurnsi yang dipasarkan. Dengan adanya informasi dari  data  nasabah  yang  ada,  perusahaan  asuransi  dapat  mengambil  suatu keputusan dalam menerapkan stragi perusahaan diantarnya yaitu menjual produk- produk promo untuk meninggatkan pendapatan perusahaan. Data mining merupakan suatu teknologi yang dapat membantu perusahaan dalam menemukan suatu yang sangat penting dari sekumpulan data. Data mining dapat membentu sautu pola atau membuat suatu sifat perilaku bisnisa yang berguna untuk pengambilan keputusan. Dengan menggunakan metode algoritma Naive Bayes diharapkan bisa membantu perusahaan dalam pengelolaan data nasabah dengan cara mengklasifikasi data nasabah untuk memprediksi minat nasabah dengan tingkat akurasi melebihi 80% dalam memilih produk asuransi meninggal dunia. Kata Kunci: asuransi, baïve bayes, prediksi, data mining.   ABSTRACTIncome for insurance companies is determined by the amount of premium paid by the customer. Important factors for customers in the form of premiums, premiums are determined in certain percentages or rates. The insurance company certainly has the amount of data, and the data is very important for companies to know the criteria of customers who are interested in the insurance marketed. With the information from existing customer data, the insurance company can make a decision in implementing the company's strategy, which is to sell promo products to increase company revenue. Data mining is a technology that can help companies find a very important set of data. Data mining can form a pattern or create a nature of business behavior that is useful for decision making. By using the Naive Bayes algorithm method, it is expected to be able to assist companies in managing customer data by classifying customer data to predict customer interest with an accuracy rate exceeding 80% in choosing a death insurance product. Keywords: insurance, baïve bayes, predictions, data mining..

scholarly journals Penerapan Algoritma Fuzzy C Means untuk Analisis Permasalahan Simpanan Wajib Anggota Koperasi

2018 ◽  
Vol 5 (2) ◽  
pp. 171
Author(s):  
Risma Rustiyan ◽  
Mustakim Mustakim
Keyword(s):  
Data Mining ◽  
Partition Coefficient ◽  
Final Stage ◽  
Fuzzy C Means ◽  
Clustering Data ◽  
Test Result ◽  
Fuzzy C Means Algorithm

<p>Koperasi mempunyai peranan penting bagi perekonomian Indonesia<strong>. </strong>Perkembangan koperasi di Indonesia saat ini cukup pesat, pada data Badan Pusat Stastitik 3 tahun terakhir yang di-<em>update</em> pada tanggal 20 Juni 2016 sementara menyebutkan jumlah koperasi aktif di Indonesia pada tahun 2015 sebanyak 150.223. Pusat Koperasi Unit Desa (PUSKUD) Provinsi Riau merupakan salah satu jenis koperasi sekunder dalam bidang pertanian. Koperasi ini menjadi salah satu penunjang perekonomian rakyat. Proses yang berjalan antara PUSKUD dan anggota adalah pengelolaan sisa hasil usaha (SHU) dan dalam permodalan. Permasalahan yang terjadi adalah partisipasi aktif dari anggota koperasi untuk menunaikan kewajiban masih kurang dari yang diharapkan. Untuk membantu mengatasi permasalahan tersebut, perlu dibentuk suatu pengelompokan Anggota PUSKUD berdasarkan Kabupaten/ Kota domisili dalam pembayaran Simpanan Wajib. Metode yang digunakan adalah <em>Data Mining Clustering</em> dengan algoritma <em>Fuzzy C Means</em>. Dari hasil pengklusteran, pada tahap akhir analisis diketahui, Terdapat 75 anggota yang tersebar pada wilayah Kabupaten/ Kota Rokan Hulu, Kampar, Indragiri Hulu dan Indragiri Hilir serta terdaftar pada tahun 80-an yang perlu untuk ditinjau  kembali. Hasil Pengujian Nilai Validitas PC, didapatkan sebesar 0,323732, dengan demikian kualitas <em>Cluster</em> masih jauh untuk mencapai kata optimal.</p><p><strong> </strong></p><p><strong>Kata kunci</strong>: <em>Clustering, Data Mining,</em><em> </em><em>Fuzzy C Means, Koperasi, Simpanan Wajib, Partition Coefficient</em></p><p class="Judul2"> </p><p class="Judul2"><strong><em>Abstract</em></strong><em></em></p><p class="Judul2"><em>Cooperatives have an important role for the Indonesian economy</em><em>. </em><em>The development of cooperatives in Indonesia is currently quite rapid</em><em>, </em><em>On the data</em><em> from Badan Pusat Statistik (BPS) </em><em> of the last 3 years updated on June 20, 2016</em><em> </em><em>While mentioning the number of active cooperatives in Indonesia in 2015 as much as 150,223</em><em>. Pusat Koperasi Unit Desa (PUSKUD) Provinsi Riau </em><em>is one of the secondary cooperative in agriculture</em><em>. </em><em>This cooperative became one of the supporting people's economy</em><em>. </em><em>The process that runs between PUSKUD dan members is the management of the remaining results of the business </em><em>and</em><em> </em><em>the</em><em> capital. The problem that occurs is the use of members of the cooperative to fulfill the obligations is still less than expected. </em><em>To help overcome these problems, it is necessary to form a grouping of PUSKUD Members by Regency / City domicile in the payment of M</em><em>and</em><em>atory Deposits</em><em>. </em><em>The method used is Data Mining Clustering with Fuzzy C Means algorithm</em><em>. From the results of the clustering, at the final stage of the analysis is known, There are 75 members scattered in the District / City of Rokan Hulu, Kampar, Indragiri Hulu dan Indragiri Hilir dan registered in the 80s that need to be reviewed. </em><em>Test Result Validity Value of Partition Coefficient, obtained for 0,323732, thus the quality of Cluster is still </em><em>far to</em><em> achieve </em><em>optimal</em><em>.</em></p><p class="Judul2"> </p><p><strong>Keywords</strong>: <em>Clustering, </em><em>Cooperative,</em><em> Data Mining, Fuzzy C Means, </em><em>M</em><em>and</em><em>atory </em><em>Deposit</em><em>, </em><em>Partition Coefficient</em></p>

scholarly journals Ensuring financial inclusion through insurance companies and credit unions

2016 ◽  
Vol 7 (1) ◽  
pp. 6-10
Author(s):  
Serhiy Kozmenko ◽  
Victoria Roienko
Keyword(s):  
Economic Growth ◽  
Financial Services ◽  
Credit Unions ◽  
Credit Union ◽  
Financial Inclusion ◽  
Insurance Companies ◽  
Insurance Company ◽  
World Regions ◽  
Economic Parameters ◽  
Economic Regions

The article analyzes modern tendencies and broadening dynamics of insurance companies’ and credit unions’ services in terms of world regions. The correlation analyses for finding lag relation between access broadening to the insurance companies’ and credit unions’ services and financial and economic parameters is held. The distribution-lag models for force and direction interrelation between access level to non-banking financial services and financial and economic regions development are elaborated. Keywords: financial inclusion, insurance company, credit union, lag, economic growth, regression

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