insurance data mining


Data mining helps insurance firms to discovery useful patterns from the customer database. The purpose of the paper aims to present how data mining is useful in the insurance industry, how its techniques produce good results in insurance sector and how data mining enhance in decision making using insurance data.

Applying data mining techniques to a health insurance information system
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This paper addresses the effectiveness of two data mining techniques in analyzing and retrieving unknown behavior patterns from gigabytes of data collected in the health insurance industry. Specifically, an episode (claims) database for pathology services and a

A Data Mining Support Environment and its Application on Insurance Data .
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Huge masses of digital data about products, customers and competitors have become available for companies in the services sector. In order to exploit its inherent (and often hidden) knowledge for improving business processes the application of data mining

Applying data mining to insurance customer churn management
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According to competition in insurance industry in Iran in recent years and entrance of private sector, keeping customers has become more important for insurer companies and reasons of churning is challenging. Thus in this research, data mining methods is used for Customer

Applications of data mining techniques in life insurance
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Knowledge discovery in financial organization have been built and operated mainly to support decision making using knowledge as strategic factor. In this paper, we investigate the use of various data mining techniques for knowledge discovery in insurance business

Data mining approaches to modeling insurance risk
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Interest in data mining techniques has been increasing recently among actuaries and statisticians involved in analysing the large data sets common in many areas of insurance . This paper discusses the use of some data mining techniques in insurance and presents two

ADLER: An Environment for Mining Insurance Data .
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The rapid technical progress of hardware and data recording technology makes huge masses of digital data about products, clients and competitors available even for companies in the services sector. Data homogenization and information extraction are the crucial tasks

Mining life insurance data for customer attrition analysis
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Customer attrition is an increasingly pressing issue faced by many insurance providers today. Retaining customers who purchase life insurance policies is an even bigger challenge since the policy duration spans for more than twenty years. Companies are eager

An exploration of classification prediction techniques in data mining : the insurance domain
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Direct mailing, as a marketing strategy for customer relationship management, faces the challenge of systematic knowledge discovery in the large database of their customers to aggressively achieve operational, tactical and a strategic advantage in this global

Use of data mining techniques to detect medical fraud in health insurance
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The health insurance claims application case the inspection usually relies on experts experience for verification and experienced personnel in charge for checking. However, due to the heavy work load and the insufficiency of manpower and experience, the ratio of

Evaluations of Data Mining Methods in Order to Provide the Optimum Method for Customer Churn Prediction: Case Study Insurance Industry
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Competitive advantage for survival and maintenance of the old companies to new companies need to identify accurately understand behavior customers. So many different ways for organizations to predict the companys customers churn. The most common

Data mining model for insurance trade in CRM system
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Data mining is used to extract meaningful information and to develop significant relationships among variables stored in large data set/ data warehouse. In this paper data mining technique named k-means clustering is applied to analyze customers preference

Mining Customers Data for Vehicle Insurance Prediction System using k-Means Clustering-An Application
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Data mining or mining customers data helps to discover the key characteristics from the customers data , and possibly use those characteristics for future prediction. The problem of selecting the best algorithm/parameter setting is a difficult one. However k-Means

Application of evolutionary data mining algorithms to insurance fraud prediction
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This study proposes two kinds of Evolutionary Data Mining (EvoDM) algorithms to the insurance fraud prediction. One is GA-Kmeans by combining K-means algorithm with genetic algorithm (GA). The other is MPSO-Kmeans by combining K-means algorithm with

Comparison of Data Mining Techniques for Insurance Claim Prediction
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This thesis investigates how data mining algorithms can be used to predict Bodily Injury Liability Insurance claim payments based on the characteristics of the insured customers vehicle. The algorithms are tested on real data provided by the organizer of the competition

DATA MINING TECHNIQUES OR IDENTI YING THE CUSTOMER BEHAVIOR O INVESTMENT IN LI E INSURANCE SECTOR IN INDIA
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Establishing a data warehouse of customer data and analyzing customer behavior have helped companies across different industries to improve their bottom line significantly. In early nineties, banking sector revolutionized the credit card industries by building its entire

DATA MINING TECHNIQUES FOR ANALYSING THE INVESTMENT BEHAVIOUR OF CUSTOMERS IN LIFE INSURANCE SECTOR IN INDIA
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Advancement of technology has paved the way for analyzing the different aspects of customers. Technology such as data ware house and data mining has made a significant contribution in almost the entire service sector where companies associated with providing

Efficient Evolutionary Data Mining Algorithms Applied to the Insurance Fraud Prediction
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Data Mining (EvoDM) algorithms to the insurance fraud prediction. One is GA-Kmeans by combining K-means algorithm with genetic algorithm (GA). The other is MPSO-Kmeans by combining K-means algorithm with Momentum-type Particle Swarm Optimization (MPSO)

Applied data mining techniques in insurance company: A comparative study of rough sets and decision tree
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Nowadays, customers are the essential elements of marketing for business operation. It is a critical and unignorable task in exploring valuable customers for companies and estimating customer values. According to the definition of Customer Life Value (denoted as CLV), a Objectives: Data mining is a horizontal technology, it can be applied in a wide range of enterprises to continuously improve business decision making. Innovation is the order of the day in the insurance industry as providers grapple with a range of business challenges.

Conceptual Mapping of Insurance Risk Management to Data Mining
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Insurance industry contributes largely to the economy therefore risk management in this industry is very much necessary. In the insurance parlance, the risk management is a tool identifying business opportunities to design and modify the insurance products. Risk can


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