Selection Bias and Auditing Policies for Insurance Claims
Selection bias results from a discrepancy between the range of estimation of a statistical model and its range of application. This is the case for fraud risk models, which are estimated on audited claims but applied on incoming claims in the design of auditing strategies. Now audited claims are a minority within the parent sample since they are chosen after a severe selection performed by claims adjusters. This article presents a statistical approach that counteracts selection bias without using a random auditing strategy. A two-equation model on audit and fraud (a bivariate probit model with censoring) is estimated on a sample of claims where the experts are left free to take the audit decision. The expected overestimation of fraud risk derived from a single-equation model is corrected. Results are close to those obtained with a random auditing strategy, at the expense of some instability with respect to the regression components set. Then we compare auditing policies derived from the different approaches. Copyright The Journal of Risk and Insurance, 2007.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 74 (2007)
Issue (Month): 2 ()
|Contact details of provider:|| Web page: http://www.wiley.com/bw/journal.asp?ref=0022-4367&site=1|
More information through EDIRC
|Order Information:||Web: http://www.wiley.com/bw/subs.asp?ref=0022-4367|
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Steven B. Caudill & Mercedes Ayuso & Montserrat Guillén, 2005. "Fraud Detection Using a Multinomial Logit Model With Missing Information," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 72(4), pages 539-550.
- Heckman, James J, 1979.
"Sample Selection Bias as a Specification Error,"
Econometric Society, vol. 47(1), pages 153-61, January.
- Dionne, G. & St-Michel, P. & Gibbens, A., 1993. "An Economic Analysis of Insurance Fraud," Cahiers de recherche 93010, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
- Dionne, G. & Gane, R. & Vanasse, C., 1995.
"Infessing Technological Parameters from Incomplete Panel Data,"
Cahiers de recherche
9537, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
- Dionne, Georges & Gagne, Robert & Vanasse, Charles, 1998. "Inferring technological parameters from incomplete panel data," Journal of Econometrics, Elsevier, vol. 87(2), pages 303-327, September.
- Dionne, G. & Gane, R. & Vanasse, C., 1995. "Infessing Technological Parameters from Incomplete Panel Data," Cahiers de recherche 9537, Universite de Montreal, Departement de sciences economiques.
- Georges Dionne & Florence Giuliano & Pierre Picard, 2003.
"Optimal Auditing for Insurance Fraud,"
Cahiers de recherche
- El Bachir Belhadji & George Dionne & Faouzi Tarkhani, 2000. "A Model for the Detection of Insurance Fraud*," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan, vol. 25(4), pages 517-538, October.
When requesting a correction, please mention this item's handle: RePEc:bla:jrinsu:v:74:y:2007:i:2:p:425-440. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Wiley-Blackwell Digital Licensing)or (Christopher F. Baum)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.