Customer expectations for faster, more convenient service have been one of the key drivers of innovation for insurers. But another consumer trend is also affecting change—lax attitudes about insurance fraud.
Americans may be showing an increasing tolerance for fraud in recent years. Case in point:
- Fewer people believe it’s unethical to misrepresent a claim to obtain payment for an uncovered loss than 20 years ago (88 percent compared to 93 percent)
- 84 percent of people think it’s unethical to submit an inflated claim, down from 91 percent in 1997
This shift—combined with insurers’ adoption of faster, digitalized claims processes—means it’s more critical than ever to embrace advanced anti-fraud technology.
Insurers face mounting pressures
Property & casualty insurance carriers face many similar challenges today, which include:
- Determining which claims to streamline for payment and which to investigate
- Operating in leaner environments with stretched resources
- Managing customer sentiment, cycle times, exposure, and litigation
Furthermore, SIU organizations are challenged by the lack of quality referrals, the number of claims they can accept, and the effectiveness of investigations. These issues put significant pressure on claims and SIU organizations, and those pressures are only increasing as resources decline, claims automation expands, and fraud rises.
Advances in anti-fraud tech
To help solve these pain points, some insurers are investing in fraud risk scoring technology. The right scoring tools may help quickly identify risks and automate detection across claims, involved parties, and medical and service providers.
How does it work? It may help to visualize a huge, specialized funnel. The funnel consumes all an insurer’s claims and scores them for risk in real-time and from first notice of loss. Only the claims with the highest propensity for fraud are slowed down and drop out the bottom of the funnel. The result provides a carrier optics for the most relevant high-risk scored claims, which are ripe for deeper investigation.
Risk scoring, particularly advanced analytics, is gaining tremendous momentum across the industry:
- 64 percent of insurers say they’re earmarking funds for predictive analytics, a 45 percent spike over 2016
- 43 percent are investing in link and social media analysis, a 27 percent increase over 2016
Using advanced analytics—such as AI and predictive analytics—to score a claim and provide the reasons supporting why a claim or party scored as it did, has significant advantages. For example, predictive modeling looks at variables (the reason behind the scores) in historical data to predict future outcomes. It employs a more proactive approach to fraud detection. The more data and results available, the better models improve scoring accuracy and identify questionable claims.
Risk scoring and reason codes are also valuable in detecting medical provider fraud, waste, and abuse. It can help investigators when interviewing providers and patients. It also guides them in reviewing records to determine if the treatment was necessary or if the provider is employing a potential fraud, waste, or abuse scheme.
Keeping pace with change
Anti-fraud technology is quickly evolving, and the wave of adoption is coming. The front end of that wave is already here. Keeping pace with these advances is essential to staying competitive and reducing fraud risk exposure.
As you explore an anti-fraud solution for your business, there are several questions you need to consider, such as whether the solution integrates with your claim system, if it automates referrals, and can you customize risk scoring based on your book of business? But, foundationally, you need to understand the data the solution analyzes, which determines the quality of the output of a predictive model.
We’ll explore the importance of data in anti-fraud solutions in the second part of this blog series.