scholarly journals A Novel Intelligence Recommendation Model for Insurance Products with Consumer Segmentation

2014 ◽  
Vol 2 (1) ◽  
pp. 16-28 ◽  
Author(s):  
Wei Xu ◽  
Jiajia Wang ◽  
Ziqi Zhao ◽  
Caihong Sun ◽  
Jian Ma
Keyword(s):  
Association Rules ◽  
Insurance Industry ◽  
Insurance Company ◽  
Growth Opportunities ◽  
Consumer Group ◽  
Transaction Data ◽  
Consumer Segmentation ◽  
Cost Of Time ◽  
Effective Product ◽  
Association Rules Analysis

AbstractAs one of the financial industries, the insurance industry is now facing a vast market and significant growth opportunities. The insurance company will generate a lot transaction data each day, thus forming a huge database. Recommending insurance products for customers accurately and efficiently can help to improve the competitiveness of insurance company. Data mining technologies such as association rules have been applied to the recommendation of insurance products. However, large policyholders’ data will be calculated when it being processed with associate rule algorithm. It not only requires higher cost of time and space, but also can lead to the final rules lack of accuracy and differentiation. In this paper, a recommendation model for insurance products based on consumer segmentation is constructed, which first divides consumer group into different classes and then processed with associate rule algorithm. The empirical results show that our proposed method not only makes the consumption of association rules analysis reduced, it has also got more effective product recommendation results.

scholarly journals Review On Application of Data Mining in Life Insurance

2018 ◽  
Vol 7 (4.5) ◽  
pp. 159
Author(s):  
Vaibhav A. Hiwase ◽  
Dr. Avinash J Agrawa
Keyword(s):  
Risk Factors ◽  
Data Mining ◽  
Life Insurance ◽  
Insurance Industry ◽  
Feature Space ◽  
Machine Learning Techniques ◽  
High Dimensional ◽  
Insurance Company ◽  
Financial Loss ◽  
Life Insurance Company

The growth of life insurance has been mainly depending on the risk of insured people. These risks are unevenly distributed among the people which can be captured from different characteristics and lifestyle. These unknown distribution needs to be analyzed from        historical data and use for underwriting and policy-making in life insurance industry. Traditionally risk is calculated from selected     features known as risk factors but today it becomes important to know these risk factors in high dimensional feature space. Clustering in high dimensional feature is a challenging task mainly because of the curse of dimensionality and noisy features. Hence the use of data mining and machine learning techniques should experiment to see some interesting pattern and behaviour. This will help life insurance company to protect from financial loss to the insured person and company as well. This paper focuses on analyzing hidden correlation among features and use it for risk calculation of an individual customer.  

scholarly journals Analisis Kepuasan Pengguna Website Asuransi Untuk Peningkatan Pelayanan Asuransi Studi Kasus www.Cakrawalaproteksi.Com

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Syafitri Mona Sari ◽  
Firdaus Firdaus ◽  
A. Haidar Mirza
Keyword(s):  
Social Media ◽  
Customer Satisfaction ◽  
Insurance Industry ◽  
Expected Value ◽  
Insurance Company ◽  
Simple Analysis ◽  
Internet Users ◽  
The Difference

Currently, technology has developed quite rapidly and covers all aspects, including in the insurance industry. Almost every insurance company has a website or social media that can be accessed by all internet users as a means of promotion and transactions. PT. Asuransi Cakrawala Proteksi is an insurance company that also carries out promotions through websites and social media. This research will discuss the customer satisfaction of PT. Asuransi Cakrawala Protection with the role of social media. Customer satisfaction is determined by looking at the difference between the actual value received and the expected value using the website and social media Facebook. From calculating the level of customer satisfaction with ServQual dimensions and simple analysis, a strategy will be produced to maintain or increase customer satisfaction.

scholarly journals Insurance Services with Special Reference to Star Health and Allied Insurance Company Limited

2019 ◽  
Vol 8 (2S8) ◽  
pp. 486-489
Keyword(s):  
Health Insurance ◽  
Insurance Industry ◽  
Insurance Company ◽  
Related Factors ◽  
Insurance Policies ◽  
Current Health ◽  
Company Limited ◽  
Major Factors ◽  
And Control ◽  
Indian Insurance

Medical coverage is budgetary instrument with which individuals are shielded against catastrophic financial weight emerging from unforeseen disease or damage. Having a well working protection system ensures pooling of assets to cover dangers. The medical coverage segment in India is in a beginning stage and a mere 9% of the complete populace is secured under any plan of medical coverage since Health Insurance policies are administrations and henceforth elusive in nature. So there is no prompt shot of acknowledging the services whether fortunate or unfortunate. Indian Insurance Industry has encountered a swelling impact after globalization and the progression of the economy. After the financial advancement, the paradigm changed from focal arranging, direction and control to showcase driven improvement. The level of buying of medical coverage shifts from individual to individual. It relies on numerous variables. The elements can be classified into individual, social, financial, mental and friends related factors. On the off chance that the health insurance business wishes to pull its weight in forming this immense market, it needs to examine the major factors impacting the buy of medical coverage arrangements, With rivalry developing perpetually, insurers need to be in the nonstop procedure of item advancement concoct inventive approaches to contribute toward actualizing the administration's need of offering medical coverage to poor. The current health insurance projects required considerable changes to make them increasingly effective and socially helpful.

scholarly journals IMPLEMENTASI ALGORITMA APRIORI TERHADAP DATA PENJUALAN PADA PERUSAHAAN RETAIL

2019 ◽  
Vol 15 (1) ◽  
pp. 85-90 ◽  
Author(s):  
Jordy Lasmana Putra ◽  
Mugi Raharjo ◽  
Tommi Alfian Armawan Sandi ◽  
Ridwan Ridwan ◽  
Rizal Prasetyo
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
A Priori ◽  
Apriori Algorithm ◽  
Business World ◽  
Transaction Data ◽  
Retail Company ◽  
Special Strategy

The development of the business world is increasingly rapid, so it needs a special strategy to increase the turnover of the company, in this case the retail company. In increasing the company's turnover can be done using the Data Mining process, one of which is using apriori algorithm. With a priori algorithm can be found association rules which can later be used as patterns of purchasing goods by consumers, this study uses a repository of 209 records consisting of 23 transactions and 164 attributes. From the results of this study, the goods with the name CREAM CUPID HEART COAT HANGER are the products most often purchased by consumers. By knowing the pattern of purchasing goods by consumers, the company management can increase the company's turnover by referring to the results of processing sales transaction data using a priori algorithm

scholarly journals Correction: Lee et al. Patterns of Multimorbidity in Adults: An Association Rules Analysis Using the Korea Health Panel. Int. J. Environ. Res. Public Health 2020, 17, 2618

2021 ◽  
Vol 18 (21) ◽  
pp. 11278
Author(s):  
Yoonju Lee ◽  
Heejin Kim ◽  
Hyesun Jeong ◽  
Yunhwan Noh
Keyword(s):  
Public Health ◽  
Association Rules ◽  
Association Rules Analysis

The authors have noticed an inadvertent error in our article, ‘‘Patterns of Multimorbidity in Adults: An Association Rules Analysis Using the Korea Health Panel” [...]

Life

2020 ◽  
pp. 8-32
Author(s):  
Benjamin Wiggins
Keyword(s):  
Public Relations ◽  
Insurance Industry ◽  
Theoretical Work ◽  
Business Practices ◽  
Insurance Company ◽  
Company Policy ◽  
History Of ◽  
A Company ◽  
History Of Race

Chapter 1 focuses on the early history of race-based insurance. When the Newark-based Prudential Insurance Company of America incorporated in 1875, it revolutionized the American insurance industry by offering policies to the working class for an affordable three cents per week. What made the Prudential doubly unique was that the company insured not simply industrial laborers, but also African American laborers. The company was not in the progressive vanguard, though. Rather, the Northern upstart, in contrast to its Southern competitors, simply had not thought to craft a company policy to explicitly ban African Americans from purchasing life insurance. Just five years after becoming the first insurer to cover black lives, the Prudential began to charge differential, race-based premiums and commenced a public relations effort to defend its discriminatory practices. This foundational chapter traces how the theoretical work of scientific racism became embedded in the business practices of American insurers.

Discovering Association Rules in Temporal Databases

2005 ◽  
pp. 195-200
Author(s):  
Juan M. Ale ◽  
Gustavo H. Rossi
Keyword(s):  
Association Rules ◽  
Temporal Databases ◽  
Transaction Data ◽  
Gathering Data

The problem of the discovery of association rules comes from the need to discover interesting patterns in transaction data in a supermarket. Since transaction data are temporal we expect to find patterns that depend on time. For example, when gathering data about products purchased in a supermarket, the time of the purchase is stamped in the transaction.

scholarly journals Occupational stress of employees in an insurance company

2006 ◽  
Vol 37 (3) ◽  
pp. 29-39 ◽  
Author(s):  
W. J. Coetzer ◽  
S. Rothmann
Keyword(s):  
Occupational Stress ◽  
Survey Design ◽  
Job Insecurity ◽  
Insurance Industry ◽  
Job Characteristics ◽  
Insurance Company ◽  
Organisational Commitment ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
And Control

The objectives of this study were to assess the internal consistency of the ASSET, to identify occupational stressors for employees in an insurance company and to assess the relationships between occupational stress, ill health and organisational commitment. A cross-sectional survey design was used. An availability sample (N = 613) of employees in an insurance company was used. An Organisational Stress Screening Tool (ASSET) was used as measuring instrument. The results showed that job insecurity as well as pay and benefits were the highest stressors in the insurance industry. Two stressors, namely job characteristics and control were statistically significant predictors of low organisational commitment. Physical ill health was best predicted by overload and job characteristics. Three stressors, namely work-life balance, overload and job characteristics best predicted psychological ill health.

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