Study of Insurance Information Base on Data Mining

2013 ◽  
Vol 380-384 ◽  
pp. 4720-4723
Author(s):  
Jian Yu ◽  
Yu Yan
Keyword(s):  
Data Mining ◽  
Insurance Industry ◽  
Rapid Development ◽  
Added Value ◽  
Insurance Companies ◽  
Insurance Company ◽  
Application Research ◽  
Mining Technology ◽  
Fierce Competition ◽  
Development Application

With the rapid development of domestic economy, the insurance industry has entered the era of fierce competition. While many domestic insurance companies face the policy a lot of business data, they do not have the corresponding ability to carry out deep analysis and mining on massive data bring added value, can not let great effect for enterprise development. Application of data mining technology in the insurance industry, can help the insurance company to improve the level of management and decision, to create a higher rate of return on investment for insurance companies and users. Application Research of the literature data about mining in insurance industry is still very few, this paper will try to explore this area, and put forward specific proposals for data mining technology in the application of insurance of our country problem.

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 Precise Position Intelligent Matching System of Online Recruitment Platform Based on Data Mining Technology

2021 ◽  
Vol 2066 (1) ◽  
pp. 012001
Author(s):  
Zhen Gao
Keyword(s):  
Data Mining ◽  
Rapid Development ◽  
Internet Technology ◽  
Job Seekers ◽  
Mining Technology ◽  
Position Information ◽  
Precise Position ◽  
Random Mass ◽  
Online Recruitment ◽  
Platform System

Abstract With the rapid development of Internet technology and computer technology, network applications have been developed more and more, and have penetrated into all walks of life in society. The emergence of the networking of the talent market has made the scale of online recruitment increase, and the amount of data on the Internet has become larger and larger, and online recruitment has become the main channel for corporate recruitment. Therefore, how to use the massive online recruitment data to quickly and accurately find the corresponding information and explore the hidden knowledge mode is a very valuable research topic. Data mining (DM) is a technology for data analysis for large amounts of data. It can discover hidden, hidden, and potentially useful knowledge hidden in the data from the vague, noisy, and random mass data, and build relevant Model, realize prediction, etc. The characteristics of data mining technology (DMT) are very suitable for the analysis of online recruitment information, research on large amounts of information, and find out the knowledge in it for decision support. This article aims to study the accurate job matching system of the online recruitment platform based on DMT. Based on the analysis of the advantages of online recruitment, related DMT and the design principles of the online recruitment platform system, the data collected by Weka DM tools are analyzed. Analyzing and getting useful job positions is just to provide job seekers and corporate-related recruiters with useful job information. The experimental results show that the online recruitment platform system can complete the collection of online recruitment position information, and can realize the DM function, which has good practical application value.

Design and Research on Data Warehouse of Insurance Industry

2014 ◽  
Vol 496-500 ◽  
pp. 2108-2111
Author(s):  
Jian Hu Zhang ◽  
Lei Lei ◽  
Xin You Cui ◽  
Yong Wu ◽  
Lin Tao Li
Keyword(s):  
Data Mining ◽  
Data Warehouse ◽  
Domain Knowledge ◽  
Insurance Industry ◽  
Basic Flow ◽  
Logical Model ◽  
Mining Technology ◽  
Concept Model ◽  
Design And Implementation

Through in-depth understanding of the domain knowledge of insurance and the study of the technology of data warehouse, the paper illustrate the application of data mining technology and data warehouse technology in the insurance clients analysis, and from the basic flow of, discusse the application of data warehouse technology in the field of insurance industry. Then, from the concept of data warehouse, describe the design and implementation of data warehouse concept model and logical model.

Road Safety Engineering: Role for Insurance Companies?

2000 ◽  
Vol 1734 (1) ◽  
pp. 7-11
Author(s):  
Sany R. Zein ◽  
Frank Navin
Keyword(s):  
Road Safety ◽  
Insurance Industry ◽  
The United States ◽  
Public Image ◽  
Insurance Companies ◽  
Safety Engineering ◽  
Insurance Company ◽  
Driver Education ◽  
Engineering Programs ◽  
Ultimate Responsibility

Over the last 10 years there has been a growing trend among automobile insurance companies to become involved in road safety engineering programs. While the involvement of insurance companies in driver education and vehicle design initiatives is common, insurance company initiatives aimed at the engineering element of road safety is a relatively new trend. This research summarizes the major road safety engineering programs undertaken by six insurance companies in Australia, Canada, and the United States, and presents some of the results achieved. The research finds that the immediacy of the benefit derived from road safety engineering improvements, coupled with an expanding knowledge base in this field, are contributing to the growth in interest in road safety among insurance companies. The financial interest of insurance companies in reducing crash frequencies and severities, as well as any related positive public image that road safety advocacy can generate, will likely mean that more insurance companies will be exploring avenues for participation in road safety programs. Opportunities exist for cooperation between the insurance industry and transportation engineers, and they should be pursued for mutual benefit. Although the ultimate responsibility and authority for roads should remain with public agencies, the incentive and emphasis that insurance companies place on road safety provide a unique opportunity to help reduce the daily risks that we face in a mobile world.

scholarly journals The Effect of Risk-Based Capital on Investment Returns of Insurance Companies in Kenya

2020 ◽  
Vol 16 (31) ◽  
Author(s):  
Willys Obuba Chache ◽  
Cyrus Iraya Mwangi ◽  
Winnie Nyamute ◽  
Caren Angima
Keyword(s):  
Linear Regression ◽  
Insurance Industry ◽  
Insurance Companies ◽  
Coefficient Of Determination ◽  
Ratio Test ◽  
Insurance Company ◽  
Risk Capital ◽  
Significance Level ◽  
Investment Returns ◽  
Capital Charge

This paper focuses on analyzing the effect of risk-based capital on investment returns of insurance companies in Kenya. The study population comprised of 63 insurance companies licensed by Insurance Regulatory Authority (IRA). A longitudinal (panel) design was used to describe the association amongst variables on the study duration. Moreover, secondary data was collected from the insurance companies’ annual returns submitted to IRA for five-year duration (2014-2018), which yielded adequate data points for each insurance company deeming it viable. Risk-based capital was determined by the standard formulae as per the risk-based supervision model. It was a composition of operational risk charge, market risk charge, insurance risk charge, credit risk capital charge, and an adjustment which considered the lossabsorbing capacity of technical provisions and deferred taxes. Investment returns in insurance companies was calculated using the investment income ratio. Test of normality, linearity, multicollinearity, and independence were conducted and were found suitable for linear regression to be conducted. Linear regression was used to evaluate the nature of the relationship between the variables based on the hypothesis in the study and at a significance level of 5%. Coefficient of determination ( ) was derived to show how the model fits the data. The study findings revealed a positive and significant relationship between risk-based capital and investment returns, thus allowing investment portfolio managers in the insurance industry to justify their investments in high risk areas that may attract a high capital charge.

scholarly journals THE IMPACT OF THE INDUSTRIAL REVOLUTION 4.0 ON THE INSURANCE INDUSTRY AND WHETHER OF THE ASSETS AND INVESTMENTS PLAY A ROLE TO INVESTMENT YIELD?

2020 ◽  
Vol 8 (2) ◽  
pp. 345-351
Author(s):  
Iskandar Muda ◽  
Hafizah ◽  
Bunga Aditi ◽  
Hermansyur ◽  
Erlina
Keyword(s):  
Financial Services ◽  
Financial Stability ◽  
Industrial Revolution ◽  
Insurance Industry ◽  
Least Square Method ◽  
Partial Least Square ◽  
Least Square ◽  
Insurance Companies ◽  
Insurance Company ◽  
The Impact

Purpose of the study: This research aims to know the influence of the Industrial Revolution 4.0 era on the insurance industry on the side of assets and Investment insurance companies to Investment Yield Sharia Insurance in Indonesia. Methodology: This type of research is explanatory research. This type of research data is secondary data sourced from the Financial Services Authority (OJK) Republic of Indonesia period in 2016-2107. The tool of analysis in this research is the Partial Least Square method using Smart PLS statistics. Main Findings: The results are an influence of Assets and Investment on Investment Yield on insurance companies in the Industrial Revolution 4.0 era. In the era of the industrial revolution, 4.0 potential insurance improve economic growth through several aspects, namely promote financial stability. Facilitate trade and commercial activities. mobilize domestic savings. Offering a variety of risk management on capital. Increase more efficient allocation of capital and reduce the risk of loss and can increase Investment Yield for shareholders and stakeholders. Applications of this study: This research is the observation only on Sharia Insurance Company sample while other issuers are not observed in this study and this research implies that sharia insurance issuers are growing and contributing to their shareholders and shareholder. Novelty/Originality of this study: The first time observing the Sharia Insurance industry industrial Revolution 4.0 era and previous research to observe in Sharia banking.

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