Fraud is not a new topic in the insurance industry. Much has been said about fraud. Fraud in recent times has become more complex and widespread in a variety of forms. Statistics show that fraud accounts for about 10 percent of the all the total losses recorded in the property industry. In the early 1980s, the insurance industry grew massively to a point where organized crime in the industry surpassed efforts to combat crime as provided for by the antifraud laws and this raised concerns over the spread of fraud (Insurance Information Institute, 2007).
Fraud can be of two types, hard or soft. At the instance where a person tampers with claims or stages an inexistent accident, it amounts to hard fraud. On the other hand, altering the provisions of legitimate complaints to evade payment of taxes or premiums amounts to soft fraud (Wood, 2014). In the insurance industry, fraudsters may range from insurers, policy takers, third-party claimants, consultants as well as the policymakers. One interesting fact from the article was the means by which fraud is propagated.
Usually, fraudsters may overstate the actual figures of the claims, state nonexistent injuries and claims, fail to deliver products and services, stage manages accidents, fake documents, misrepresent data as well as tamper with evidence. Over time, the healthcare, labor unions, as well as the motor industry, have been found to be the most affect industries with insurance fraud.
How fraud is propagated in the present times is quite sophisticated mainly because of the immense influence of technology. While reading through the article, it surprised me to realize the fact that many insurers have adopted the use of antifraud technology primarily meant to evade fraud.
It is impossible for enterprises at present to conduct operations without taking into account measures to mitigate fraud risks. Given this, a good number of businesses have employed predictive modeling (Insurance Information Institute (2007). However, many organizations have not yet adopted the technology while many of them have not found a suitable technology to mitigate fraud losses. The lack of sufficient technique to maintain and cove fraud instances exhaustively is still a gruesome challenge to many organizations.
Keep in mind that even as technology keeps changing, fraud keeps changing and advancing too (Kurvinen, Ilkka & Murthy, 2016). A survey conducted by Property Casualty Insurers Association of America and the FICO shows that about five to ten percent of the total claim costs are enshrined in fraud cases. However, many still believe that fraud cases are more than what the statistics state.
The takeaway message from the article is that fighting insurance fraud is dependent on the weight with which the legislators, regulators and law enforcers place on the fraud mitigation as well as the allocation of the appropriate fraud mitigation resources in the insurance industry. In my opinion, there is need to embrace the new technology moves such as data mining programs to combat fraud. Over 30 percent of the business failures and bankruptcy claims are out of deception (Power & Power, 2015).
When fraud is propagated, everybody suffers. The nation, businesses, retailers, society and innocent people suffer when insurance fraud is spread. When insurance companies pass the cost of fraud to a policyholder, premiums rise thereby hurting consumers. The exciting bit is that data analysis is only able to flag cases but cannot prove fraud.
Increased surveillance, stakeouts as well as the social media can help spot and unearth fraudsters. In severe cases, hands-on investigations especially private investigation be used. The management should improve on the method of collection of data and ensure that it only collects quality information.
Robust anti-fraud processes and procedures should be incorporated as part of the risk management strategies adopted by the company (Kenyon & Eloff, 2017). Ethical standards should be utilized in the organization. It starts with the top management and trickles down to the middle and junior level management.
Insurance Information Institute (2007, November 6). Background on: Insurance Fraud. Retrieved from https://www.iii.org/article/background-on-insurance-fraud. Accessed on [4 Feb, 2018].
Kenyon, D., & Eloff, J. H. P. (2017, August). Big data science for predicting insurance claims
Kurvinen, M., Ilkka, T. Ã., & Murthy, D. P. (2016). Warranty fraud management: reducing fraud and other excess costs in warranty and service operations. John Wiley & Sons.
Power, D. J., & Power, M. L. (2015, May). Sharing and Analyzing Data to Reduce Insurance Fraud. In Proceedings of the 10th Annual MWAIS Conference (MWAIS 2015), Pittsburg, KS, USA.
Wood, S. (2014). Fraud Facts: The real cost of insurance fraud – and what we can do.[online]. Retrieved from http://nydailyrecord.com/2014/05/11/fraud-facts-the-real-cost-of-insurance-fraud-and-what-we-can-do/.Accessed [Feb 5, 2018].