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Title
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Trend features and additive feature selection methods for churn models under property and casualty insurance business paradigms.
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Author
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Anderson, Jacob Foster
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Abstract/Description
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ABSTRACT: The P&C Small Commercial Insurance industry presents a signiαcant policy retention challenge given the presence of independent intermediaries. Current retention models generally follow traditional relational marketing paradigms and therefore do not account for the complexities introduced by the presence of an independent intermediary. Given an anonymous policy dataset consisting of correlated, high-dimensional, and high-cardinality categorical/numeric data; current data mining...
Show moreABSTRACT: The P&C Small Commercial Insurance industry presents a signiαcant policy retention challenge given the presence of independent intermediaries. Current retention models generally follow traditional relational marketing paradigms and therefore do not account for the complexities introduced by the presence of an independent intermediary. Given an anonymous policy dataset consisting of correlated, high-dimensional, and high-cardinality categorical/numeric data; current data mining methods are used to construct a retention model with practical applications. Additionally, predictive features that capture intermediary-related information are engineered and designated as candidate features. Candidate features are selected for αnal model inclusion using various data mining approaches to feature importance measurement and feature selection.
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Date Issued
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2018.
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Identifier
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1128024057, WFE0000646
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Format
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Document (PDF)