Financial Risk Management
Predictive Analytics for Insurer Risk Management: Behavioral Traits of Fraudsters

Article Details
Pub. Date : Sep, 2019
Product Name : The IUP Journal of Financial Risk Management
Product Type : Article
Product Code : IJFRM51909
Author Name : V Padmavathi and Ishaan Sengupta
Availability : YES
Subject/Domain : Finance Management
Download Format : PDF Format
No. of Pages : 11



Insurance organizations around the world have often come across individuals or companies which are their insureds, trying to claim much more than what they are liable to receive after the standard deduction. These are cases which fall under the bracket of fraudulent claims. Such cases prompted the insurance firms to set up investigation teams to verify the authenticity of the claim. However, despite having improved the loss percentage, the Special Investigation Teams (SIT) of insurance companies could not predict potential fraudsters. It is after this that predictive analytics was used to profile insureds based on a huge number of parameters. Despite this development, insurance organizations still lose 10% of the claims as they are fraudulent in nature. What can really aid in the reduction of losses is the ability of the organization to profile applicants and the insured based on behavioral traits. To address this issue, what better than to use the tools already at the disposal of insurance companies for profiling the same? This paper addresses the importance of globally accepted psychometric tests to assist insurance organizations in knowing more about the clientele they intend to serve.


Behavioral science and econometrics have often gone hand in hand in establishing cogent relations between behavioral traits and their impact on certain managerial tools. However, the use of the same behavioral tools has not seen the light of the day in emphasizing or analyzing the risks involved in insurance fraud. The paper takes into consideration two major psychometry tests, namely, the dark triad and the fraud triangle, and establishes a relationship between these traits and the risk level of the person claiming an insurance reimbursement. This is unique as no research in the same field has been done with quantitative models backing each trait.


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