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Predicting customer level risk patterns in non-life insurance
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics. (Matematisk statistik)
2012 (English)Independent thesis Advanced level (degree of Master (One Year)), 20 credits / 30 HE creditsStudent thesisAlternative title
Prediktering av riskmönster på kundnivå i sakförsäkring (Swedish)
Abstract [en]

Several models for predicting future customer profitability early into customer life-cycles in the property and casualty business are constructed and studied. The objective is to model risk at a customer level with input data available early into a private consumer’s lifespan. Two retained models, one using Generalized Linear Model another using a multilayer perceptron, a special form of Artificial Neural Network are evaluated using actual data. Numerical results show that differentiation on estimated future risk is most effective for customers with highest claim frequencies.

 

Place, publisher, year, edition, pages
2012. , 42 p.
Series
Trita-MAT, ISSN 1401-2286 ; 2012:24
Keyword [en]
Predictive Modeling, Generalized Linear Models, Artificial Neural Networks.
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:kth:diva-103590OAI: oai:DiVA.org:kth-103590DiVA: diva2:560786
Subject / course
Mathematical Statistics
Educational program
Master of Science in Engineering -Engineering Physics
Uppsok
Physics, Chemistry, Mathematics
Examiners
Available from: 2012-10-15 Created: 2012-10-15 Last updated: 2012-10-15Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf