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Insurance fraud is something that affects all aspects of insurance. Fraud, according to merriam-webster.com, is “using dishonest methods to take something valuable from another person.” (2015) in this presentation, we will discuss who is affected by fraud, different types of fraud, why fraud is an issue, and how we can help stop fraud.

Consumers, insurance companies, innocent “victims” in accidents, police and district attorneys are all affected by fraud of different types. There are many reasons why insurance fraud is an issue. Insurance companies pay out on fraudulent claims, which then cause premiums to increase for consumers. A study conducted by the Coalition Against Insurance Fraud shows that consumers see an increase of between 13% and 18% annually in premiums as a result of insurance fraud. (insurance-fraud.org, 2015) This means that of every $100 dollars consumers pay in premium, between $11.50 and $15.25 of this is due to insurance fraud.

Victims in car accidents can be affected because the insurance company may not pay a valid claim due to fraudulent actions by one or both parties in a loss. Police and district attorneys have to use valuable resources to combat against fraud.

The Top Questionable Insurance Claims

With insurance fraud being so prevalent the Insurance Information Institute released statistical data on the top questionable insurance claim categories that have been increasing year over year. Personal Property claims take top billing when it comes to fraud claims. Included in this section is homeowners insurance and renters insurance. Not only in the residential world do we have claims but in the business world as well with Commercial property claims coming in second place and workers compensation claims comes in a strong third place. These claims seem to be easier to get away with fraud due to the lack of property or injuries that can be claimed.

Data analysis

With a $40 billion bill, collecting data about fraudulent claims becomes a long and tedious task, of which the benefits of collection are not without reward. Interpreting the results can lead to an understanding of its origin, which can be avoided by using preventive measures to identify and counter fraudulent claims. "Association Rules graph for Fraud Detection using sophisticated data mining tools such as decision trees, machine learning, association rules, cluster analysis and neural networks, predictive models can be generated to estimate things such as probability of fraudulent behavior or the dollar amount of fraud" (Fraud Detection, 2015). By implementing data collection and statistical analysis practices it is possible to develop estimated predictions of fraudulent probability. "In insurance, 25% of claims contain some form of fraud, resulting in approximately 10% of insurance payout dollars" (Fraud Detection, 2015). For insurance providers, collecting this data is their first step toward preventing fraudulent claims, raising their profitability and being able to offer competitive consumer rates. 

In terms of various parameters or probability distributions, there are many things that should be considered when identifying the parameters for fraudulent likelihood such as: averages, quantiles and performance metrics. Averages may include the length of a call, average number of calls per month and average delays in bill payment (Fraud Detection, 2015). Recognizing trends among those who commit fraudulent claims may allow the insurers to more easily identify those with a tendency to commit fraud establishing parameters and establishing probability can aid in reducing the costs of fraud and auditing by knowing the cues of fraud. "In fraud detection, wrongly classifying a legitimate claim to the fraud class may just result in unnecessary auditing cost, whereas missing a fraudulent claim could lead to a substantial amount of unnecessary claim payments being made by the insurance company" (Ai, 2010). Becoming aware of the information, probability and parameters of fraud occurrences by collecting data can ultimately help reduce costs, increase profits and solicit a larger customer base because there is less loss incurred by the company.

Reference

Fraud statistics. (2015.). Retrieved March 29, 2015, from http://www.insurancefraud.org/statistics.htm#Auto insurance

Fraud Detection. (2015). Retrieved April 20, 2015, from http://www.statsoft.com/textbook/fraud-detection

Brockett, P., & Golden, L. (2010, April 1). Assessing Consumer Fraud Risk in Insurance Claims. Retrieved April 20, 2015, from http://jingai.shidler.hawaii.edu/downloads/assessing-consumer-fraud-risk-in-insurance-claims.