Targeting customers via discovery knowledge for the insurance industry

Wu, Chien-Hsing; Kao, Shu‐Chen; Su, Yann-Yean; Wu, Chuan-Chun

doi:10.1016/j.eswa.2005.04.002

Cited by 32 publications

(18 citation statements)

References 21 publications

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“…In the first field, the customers of a company are classified according to their characteristics in order to estimate their degree of acceptance of certain product, new or into the portfolio (see, e.g. Wu et al 2005;Abrahams et al 2009or Hsu 2010. Wu et al (2005) use KDD/DM (knowledge discovery in databases and data mining) to look for the rules that allow to identify potential customers for new or existing products of an insurance company.…”

Section: Background About Classificationmentioning

confidence: 99%

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Some criticism to a general model in Solvency II: an explanation from a clustering point of view

2016

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It is a well-known fact that heterogeneity is one of the characteristics of the insurance market, and it is relevant to classify and characterize companies by means of their financial properties and different risk profiles. So, it may not be adequate to use a general model for all the companies operating in the European market, as the one proposed by the Directive 2009/138/CE. Solvency II is a general regulatory model such that the volume of own resources will be determined depending on risks based on a calibration reached considering the average behaviour of companies. In order to criticize this approach, we have obtained a characterization of the profiles of companies using a PAM clustering methodology, adapted for longitudinal data, and we have studied the evolution of the obtained groups of companies under a Bayesian approach. In this way, we have introduced a multinomial general dynamic linear model to study the probabilities of the companies to be included into each group. The characterization and identification of these groups suggest that an unique regulatory model may be unsuitable. We have used data from DGSFP (Spanish insurance regulator), with public information about the balance sheets and income statements from years 1999 to 2011.

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Section: Background About Classificationmentioning

confidence: 99%

“…Wu et al 2005;Abrahams et al 2009or Hsu 2010. Wu et al (2005) use KDD/DM (knowledge discovery in databases and data mining) to look for the rules that allow to identify potential customers for new or existing products of an insurance company. They use algorithms such as ID3 (Quinlan 1986).…”

Section: Background About Classificationmentioning

confidence: 99%

Some criticism to a general model in Solvency II: an explanation from a clustering point of view

2016

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“…If too small, a group of granulation may not be able to contain acceptable number of instances, and consequently the discovered results may be meaningless. In spite of full freedom for a granulation method, the number of granules should be carefully chosen to eliminate unnecessary problems [28]. By a discussion with the case company experts, the granulation operation was based on the granulation regulations that are listed in Table 2.…”

Section: Case Company Data and Data Preparation And Selectionmentioning

confidence: 99%

“…In [28], Wu et al used the knowledge discovery in databases and data mining (KDD/DM) to help targeting customers in the insurance industry. A case study of KDD/DM was performed to explore decision rules from company's data base.…”

Section: Using Dm In Insurance Industrymentioning

confidence: 99%

Labeling Customers Using Discovered Knowledge Case Study: Automobile Insurance Industry

Azadmanesh¹,

Tarokh

2012

IJMVSC

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In this paper, we used the knowledge discovery in databases and data mining, one of the data-based decision support techniques to help labeling customers in the automobile insurance industry. In most data mining application cases, major tasks including data preparation, data preprocessing, data transformation, data mining, interpretation, application and evaluation, are Keywords labeling customers, discovered knowledge, decision tree, ID3 IntroductionCustomer relationship management (CRM) includes a set of processes and it enables systems to support a business strategy to build long term, profitable relationships with specific customers [11]. Customer data and information technology (IT) tools form the foundation of any successful CRM strategy. Also, the rapid growth of the Internet and its connected technologies has extremely increased the opportunities for marketing and transformed the way that relationships between companies and their customers are managed [14].From the architecture point of view, the CRM framework can be classified into operational and analytical ([1]; [8]; [21]). Operational CRM apply to the automation of business processes, while analytical CRM refers to the analysis of customer characteristics and behaviors to support the organization's customer management strategies. Analytical CRM could help an organization to better separate and more effectively allocate resources to the most profitable group of customers. Data mining tools are a popular means of analyzing customer data within the analytical CRM framework. Many organizations have collected and stored a wealth of data about their current customers, potential customers, suppliers and business partners. However, the inability to explore valuable information hidden in the data prevents the organizations from transforming these data into valuable and useful knowledge [1]. In order to effectively reveal information in handling data, information technology has introduced the development and application of the techniques of knowledge discovery (KD) and data mining (DM). KD/DM is basically a dataoriented method that merges database management, knowledge representation, and machine

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“…It has become very important because of an increased demand for methodologies and tools that can help the analysis and understanding of huge amounts of data generated on a daily basis by institutions. Knowledge discovery has been successfully used in various application areas: engineering [1], education [2], business and finance [3], insurance [4], telecommunication [5], chemistry [6], and medicine [7]. There are many techniques which are used for knowledge extraction from databases such as neural networks [8][9], genetic algorithms [10][11], decision tree [12][13], instance-based learning [14][15], rule induction [16][17], and support vector machine [18][19].…”

Section: Introductionmentioning

confidence: 99%

Destructive Algorithm for Rule Extraction based on a Trained Neural Network

ElAlami¹

2012

IJCA

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The present paper introduces a new destructive algorithm for rule extraction based on a trained neural network. The degree of complexity of neural network increases exponentially as a factor of the numbers of input and hidden nodes. Therefore, the dimensionality of the trained neural network is reduced by using a proposed destructive algorithm to extract only the most effective values of the input attributes which have higher impact on the output result for each class. Thus, the searching efficiency is highly increased and the computation is dramatically reduced for extracting rules. The generated rules from the proposed model are fired through two levels for each class. As for the first level, it deals with each individual effective input value, and the second level is concerned with each possible conjunction of the effective input values. Moreover, the proposed model extracts the strongest rules which represent a large number of instances from the database by adjusting the similarity measure threshold value. Finally, the proposed model is evaluated on different public-domain datasets and compared with standard learning models from WEKA, then the results assert that the set of rules extraction from the proposed method is more accurate and concise compared with those obtained by the other models.

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Targeting customers via discovery knowledge for the insurance industry

Cited by 32 publications

References 21 publications

Some criticism to a general model in Solvency II: an explanation from a clustering point of view

Some criticism to a general model in Solvency II: an explanation from a clustering point of view

Labeling Customers Using Discovered Knowledge Case Study: Automobile Insurance Industry

Destructive Algorithm for Rule Extraction based on a Trained Neural Network

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