Notice of Retraction: Analysis on application of data mining upon rate making and risk analysis in insurance

Xiahou, Jianbing; Mu, Yang

doi:10.1109/iccet.2010.5485538

Cited by 2 publications

(1 citation statement)

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“…The best modeling in these circumstances is one which using methods from machine learning and data mining (Christmann, 2004). In recent years many papers deal with the application of data mining methods for loss cost estimation and risk analysis in insurance (Xiahou & Mu, 2010;Guelman, 2012;Thakur & Sing, 2013;Huo, Wang & Yang, 2013).…”

Section: Review Of Literaturementioning

confidence: 99%

A Data Mining Approach for Risk Assessment in Car Insurance

Kašćelan

Novović-Burić

2014

International Journal of Business Intelligence Research

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This paper has proposed a data mining approach for risk assessment in car insurance. Standard methods imply classification of policies to great number of tariff classes and assessment of risk on basis of them. With application of data mining techniques, it is possible to get functional dependencies between the level of risk and risk factors as well as better results in predictions. On the case study data it has been proved that data mining techniques can, with better accuracy than the standard methods, predict claim sizes and occurrence of claims, and this represents the basis for calculation of net risk premium and risk classification. This paper, also, discusses advantages of data mining methods compared to standard methods for risk assessment in car insurance, as well as the specificities of the obtained results due to small insurance market, such is the one in Montenegro.

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Section: Review Of Literaturementioning

confidence: 99%

A Data Mining Approach for Risk Assessment in Car Insurance

Kašćelan

Novović-Burić

2014

International Journal of Business Intelligence Research

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Reduction of Risk Factors in Risk Assessment: An Identification Tree Approach

Agarwal¹,

Dongardive²,

Abraham³

2013

IJMO

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Abstract-The paper proposes a unique procedure to reduce risk factors in risk assessment. It offers a variant of decision tree called Identification tree for reducing number of risk factors used in assessment. The model, which uses auto insurance as a case study, employs historical evidences of different vehicles as risk factors. The work offers reduction of original risk factors from a set of twenty three to a reduced set of nine risk factors. The model was validated using real time and industry specific data.

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Notice of Retraction: Analysis on application of data mining upon rate making and risk analysis in insurance

Cited by 2 publications

References 1 publication

A Data Mining Approach for Risk Assessment in Car Insurance

A Data Mining Approach for Risk Assessment in Car Insurance

Reduction of Risk Factors in Risk Assessment: An Identification Tree Approach

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