Abstract
In this paper we apply the weight of evidence reformulation of AdaBoosted naive Bayes scoring due to Ridgeway et al. (1998) for the diagnosis of insurance claim fraud. The method effiectively combines the advantages of boosting and the modelling power and representational attractiveness of the probabilistic weight of evidence scoring framework. We present the results of an experimental comparison with an emphasis on both discriminatory power and calibration of probability estimates. The data on which we evaluate the method consists of a representative set of closed personal injury protection automobile insurance claims from accidents that occurred in Massachusetts during 1993. The findings of the study reveal the method to be a valuable contribution to the design of effective, intelligible, accountable and efficient fraud detection support.
Keywords
- Receiver Operating Characteristic Curve
- Probability Estimate
- White Collar Crime
- Fraud Detection
- Insurance Fraud
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, access via your institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Viaene, S., Derrig, R., Baesens, B., Dedene, G.: A comparison of state-of-the-art classification techniques for expert automobile insurance fraud detection. Journal of Risk and Insurance (2002) to appear
Ridgeway, G., Madigan, D., Richardson, T., O'Kane, J.: Interpretable boosted naive Bayes classification. In: Fourth International Conference on Knowledge Discovery and Data Mining, New York City (1998)
Weisberg, H., Derrig, R.: Identification and investigation of suspicious claims. AIB Cost Containment/Fraud Filing DOI Docket R95-12, AIB Massachusetts (1995) http://www.ifb.org/ifrr/ifrr170.pdf
Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Machine Learning 29 (1997) 131–163
Kohavi, R., Becker, B., Sommerfield, D.: Improving simple Bayes. In: Ninth European Conference on Machine Learning, Prague (1997)
Domingos, P., Pazzani, M.: On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning 29 (1997) 103–130
Freund, Y., Shapire, R.: A decision-theoretic generalization of on-line learning and an application to boosting. In: Second European Conference on Computational Learning Theory, Barcelona (1995)
Bauer, E., Kohavi, R.: An empirical comparison of voting classification algorithms: Bagging, boosting and variants. Machine Learning 36 (1999) 105–139
Shapire, R., Freund, Y., Bartlett, P., Lee, W.: Boosting the margin: A new explanation for the effectiveness of voting methods. The Annals of Statistics 26 (1998) 1651–1686
Elkan, C.: Boosting and naive Bayesian learning. Technical Report CS97-557, Department of Computer Science and Engineering, University of California, San Diego (1997)
O'Kane, J., Ridgeway, G., Madigan, D.: Statistical analysis of clinical variables to predict the outcome of surgical intervention in patients with knee complaints. Statistics in Medicine (1998) submitted
Good, I.: The estimation of probabilities: An essay on modern Bayesian methods. MIT Press, Cambridge (1965)
Spiegelhalter, D., Knill-Jones, R.: Statistical and knowledge-based approaches to clinical decision-support systems, with an application in gastroenterology. Journal of the Royal Statistical Society. Series A (Statistics in Society) 147 (1884) 35–77
Provost, F., Fawcett, T., Kohavi, R.: The case against accuracy estimation for comparing classifiers. In: Fifteenth International Conference on Machine Learning, Madison (1998)
Hand, D.: Construction and assessment of classification rules. John Wiley & Sons (1997)
Hanley, J., McNeil, B.: The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143 (1982) 29–36
Provost, F., Fawcett, T.: Robust classification for imprecise environments. Machine Learning 42 (2001) 203–231
Bishop, C.: Neural networks for pattern recognition. Oxford University Press (1995)
Titterington, D., Murray, G., Murray, L., Spiegelhalter, D., Skene, A., Habbema, J., Gelpke, G.: Comparison of discrimination techniques applied to a complex data set of head injured patients. Journal of the Royal Statistical Society. Series A (Statistics in Society) 144 (1981) 145–175
Spiegelhalter, D.: Probabilistic prediction in patient management and clinical trials. Statistics in Medicine 5 (1986) 421–433
Copas, J.: Plotting p against x. Journal of the Royal Statistical Society. Series C (Applied Statistics) 32 (1983) 25–31
Bennett, P.: Assessing the calibration of naive Bayes’ posterior estimates. Technical Report CMU-CS-00-155, Computer Science Department, School of Computer Science, Carnegie Mellon University (2000)
Zadrozny, B., Elkan, C.: Learning and making decisions when costs and probabilities are both unkown. In: Seventh ACM SIGKDD Conference on Knowledge Discovery in Data Mining, San Francisco (2001)
Ridgeway, G., Madigan, D., Richardson, T.: Boosting methodology for regression problems. In: Seventh International Workshop on Artificial Intelligence and Statistics, Fort Lauderdale (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Viaene, S., Derrig, R., Dedene, G. (2002). Boosting Naive Bayes for Claim Fraud Diagnosis. In: Kambayashi, Y., Winiwarter, W., Arikawa, M. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2002. Lecture Notes in Computer Science, vol 2454. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46145-0_20
Download citation
DOI: https://doi.org/10.1007/3-540-46145-0_20
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44123-6
Online ISBN: 978-3-540-46145-6
eBook Packages: Springer Book Archive