Unlocking the future of SIUs success

How advanced analytics and automation can help combat insurance fraud

December 4, 2023

Special Investigation Units (SIUs) and their leaders face unprecedented challenges in today’s rapidly evolving digital landscape. The insurance industry is contending with increasingly sophisticated financial crimes. Moreover, insurers grapple with mounting expectations from policyholders, regulators and other stakeholders to identify suspicious claims earlier in the claim lifecycle. Meeting these demands requires expediting the investigation process to distinguish high-risk claims and pay legitimate ones faster — all while coping with reduced staffing levels. Ultimately, it’s important for SIUs to continuously evaluate their current processes. 

To gain insight into how insurers measure and enhance performance in anti-fraud investigations, we conducted peer-group analysis in collaboration with the Coalition Against Insurance Fraud (CAIF). The Keys to Unlocking SIUs Future Success assesses the collective performance within the SIU industry and identifies opportunities for advancement and improvement to help insurers better prepare for the future.  

Fighting fraud is a priority across the insurance industry  

Fraudsters adapt rapidly, often submitting countless “undetected” claims and conducting fraudulent activities that impact the entire industry before insurers can establish safeguards or response systems. Consumers shoulder the burden through higher premiums, personal injuries and damages. Ultimately, it’s a crime we all end up paying for. Insurance fraud costs the U.S. $308 billion annually, according to recent research from CAIF.  

As the insurance industry continues to grow and regulations evolve, companies, regardless of premium size and line of business, have realized the importance of creating an investigative team. An overwhelming 99% of those surveyed indicate that they have a formal SIU to investigate suspected insurance fraud. This is compared to 87% of insurers in a similar CAIF study conducted in 2003.  

Measuring the effectiveness of special investigation teams 

Every insurer strives to have industry leading claims and investigation performance. To achieve this, it’s crucial to establish clear standards for evaluating anti-fraud metrics such as overall detection, conversion, acceptance rates and false positive ratios. The challenge lies in striking the correct balance: swiftly and consistently identifying potential fraud while developing new methods for straight-through processing (STP) that can help reduce manual touchpoints and costs.    

As SIUs adapt to growing challenges, it’s essential to enhance their performance through rigorous evaluations. A substantial percentage (88%) of companies reported that they evaluate the overall effectiveness of their fraud investigative program and most (82%) calculate the economic impact of their SIU investigations. This emphasizes the industry's commitment to continually improving fraud detection and prevention, along with a rise in regulatory and operational scrutiny of anti-fraud efforts.  

Transforming how insurers protect themselves through emerging tech  

As fraudsters become more sophisticated, insurers require advanced tools to help stay ahead. Advanced analytics allow companies to modernize existing fraud prevention strategies, enabling them to quickly determine suspicious indicators during the straight-through processing of claims. Of the insurers surveyed, 67% confirmed that they’re using advanced analytics to try to enhance their ability to detect fraud. Advanced analytics can facilitate automation with the potential to streamline operations and more efficiently direct suspicious claims to SIUs for investigation.  

Most insurers use a mix of internal resources and external vendors for analytics. This offers an advantage for companies with in-house analytics to complement them with vendor solutions for a more thorough anti-fraud strategy.  

It’s noteworthy that more than half of insurers currently lack the ability to automate referrals to their investigative teams. Insurers employing straight-through processing may consider placing more emphasis on analytics to exclude claims without apparent suspicious indicators. This can be achieved through a combination of unstructured claims data, business rules, past instances of suspected fraud, third-party data and specific claim types.

Additionally, understanding referral cancellations should be a top priority. Reducing referral cancellations frees up time and enhances the efficiency of investigative teams, allowing them to expedite those claims with clear indicators of suspicion — which ultimately benefits policyholders and claimants.  

Raising the bar on detecting suspicious claims 

Most states require insurers to report suspicious claims. The challenge is identifying potential insurance fraud which, by its very nature, is concealed. Emerging technologies can help uncover patterns and trends that are often indicative of suspicious claims. Currently, only a fraction of insurers (33%) detect more than 3% of new claims as suspicious while 47% detect less than 3%. Key fraud organizations such as NICB, III, the Coalition, NAIC and IASIU estimate around 10% of all claims are potentially fraudulent, according to CAIF’s study The Impact of Insurance Fraud on the US Economy. This implies that a significant number of suspicious claims might be going undetected. 

Instead of accepting fraud-related losses as the cost of doing business, insurers should seek innovative strategies to help improve their operations for enhanced effectiveness in the struggle against fraudsters. Automation and advanced analytics hold the potential to enhance investigation processes, helping to mitigate potentially fraudulent claims and improve the ability to provide customer satisfaction while decreasing the amount and impact of insurance fraud. In a world of ever-evolving financial crime, bolstering our response to suspicious claims is imperative for the future success of SIUs.  

Reach out for full survey information.

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