SIUs Detect Soft Fraud with Technology

Unlike the criminals guilty of ‘hard’ fraud—such as the ones we often report about on our blog involving PIP insurance schemes through staged car accidents—there is another group of people who commit ‘soft’ fraud without fully realizing the impact it has on increasing their insurance costs.

Soft fraud, also known as opportunity fraud, is the bane of the insurance industry and the Special Investigative Units (SIUs) that serve them. The little lies, exaggerations, and exclusions that make up this type of fraud are difficult to detect, and when they are uncovered, are often difficult to investigate without offending long-term customers.

According to a commentary by Joseph Bracken on InformationWeek Insurance & Technology, soft fraud includes circumstances like:

• Overstating the extent or origin of damage when filing a claim
• Overestimating the value
• Minimizing annual mileage driven
• Neglecting to mention the existence of teenage drivers

Consumers’ tolerance to soft fraud has been shown in an Insurance Research Council 2013 study in which 24 percent of respondents thought it was acceptable to overstate the amount of a claim submission as a way to offset the cost of a deductible, and 10 percent said that insurance fraud “doesn’t hurt anyone.”

However, the Coalition Against Insurance Fraud, reveals just how much insurance fraud hurts—it costs the economy $80 billion annually, which is enough to cover salaries of 2.2 million American workers for a year.

This is where analysis using technology can come into play, Bracken says, to help SIUs protect policyholders from soft fraud, including the following:

1. Establish a baseline on the number of claims being investigated, keeping in mind that based on industry data, about 10-20% of insurance claims have the potential to be fraudulent.
2. Establish patterns of activity according to type of claim or claimant demographic to identify inconsistencies.
3. Set up business rules and maximum limits to identify the claims that need closer review, basing them on claim characteristics (exceeding a certain dollar limit) or insured behavior (such as a change in the coverage limit up to 60 days before a claim).
4. Develop norms for common claims and flag those that veer from the norm.
5. Compare in-house and third-party data to identify inconsistent behavior.
6. Compare average retail store values with claim values.
7. Eliminate data silos so investigators can merge data from different sources within the company.

While this analytical investigative approach may seem a heavy-handed way to combat soft fraud, it ends up being an essential component to achieving several goals—it’s successful in detecting claims that are most likely to be fraudulent, it’s cost-effective for SIU investigators to zero in on those claims, and it ultimately decreases premium costs.