aureus-insights_logo

All Posts

Using Analytics To Counter Early Claim Fraud

According to the Coalition Against Insurance Fraud, at least $80 billion in fraudulent claims are made annually in the U.S across all lines of insurance. This translates more than $400-$700 per year in increased premiums for each American family.

Fraud in insurance has been in existence since the inception of the industry. Of late, however, modus operandi has become more sophisticated and the fraudsters have been able to rapidly adapt to changing anti-fraud measures.

Why is Insurance Fraud so Hard to Catch?

  1. Ideally, fraud should be controlled at the proposal stage, not the claim stage. Hence there is a need to strengthen the underwriting processes. But most insurers are still saddled with the legacy systems and their limitations make it difficult to identify fraud at point of sale / log in stage / underwriting stage.
  2. The burden of fraud is eventually borne by the larger policy base in form of increased premium, longer policy issuance cycles, longer claims cycles etc… just to name a few. This in turn adversely impacts the overall customer experience as every customer is looked at with the same lens.
  3. Increasing fraud impacts the financial viability of the company by paying out invalid and fraudulent claims.
  4. Due to increasing fraud, the current process of claims management involves claim investigations through outsourced vendors which takes time to assess and identify fraud. It also burdens the risk management team as they must investigate all claims however big or small with the same approach. As a result, claim processing becomes time consuming.
  5. With increasing fraud in the industry, assessing whether a customer is genuine or not is a big challenge. In the quest of identifying fraud, the claim investigators, at times, lose sight of straight-forward and genuine claims. This delays the claim settlement process leading to negative customer experience and therefore defeating the basic purpose of insurance.
  6. Insurers lack robust systems in place that caters to identifying and controlling fraud. In the current scenario, insurers are reactive and find solutions once the fraud has occurred.  To combat this rising fraud dilemma, insurers need to be proactive by detecting fraud before it happens or as soon as it happens so that the correct action can be taken at the right time.

 When does Fraud Happen?

Fraud generally occurs at two points in the policy life cycle:

  1. New Policy Issuance
  2. Claims Stage

Earlier, insurance fraud was restricted to suppression of material facts and padding or inflating of damages which could lead to higher renewal premiums or refusal to issue a policy. In recent years, the pattern of fraud has changed, and insurers are witnessing a greater number of more complex fraud like below:

  1. A policy taken out on a person who has died before policy issuance;
  2. Insurance coverage for a non-existent entity;
  3. Policy taken out intentionally on a critically ill person;
  4. Multi-insurance fraud: over insuring by taking multiple policies from several insurance companies at the same time;
  5. Willful wrong categorization of damages (e.g. claiming flood damage as theft, or fire damages) etc…

Early claim fraud is making a lot of insurers anxious. ‘Early claims’ are defined as life claims that come within 2-3 years of policy issuance. While claims in themselves impact the insurer’s overall profitability, carriers are usually prepared for them. Based on previous years’ experience, carriers mark out reserves that would be used for any claim event. Early claims, on the other hand, throw a wrench in the works for carriers. These are claims that were not anticipated so early in the policy life cycle.

Proportion of early claim fraud are typically of the order of 1% or less of the policy portfolio.

According to the Insurance Information Institute, healthcare, workers compensation and auto insurance, are most vulnerable to insurance fraud.

Using analytics to tackle Early Claims Fraud

The type of fraud at each stage varies and requires a different anti-fraud approach. Most insurance carriers use a flag based / business rules driven anomaly detection approach as the first fraud filter. However, this approach has a higher probability of identifying false positives, which can impact the overall customer experience. Another challenge with this approach is that the frequency of refreshing these business rules may not be often enough. As per a report by the Insurance Fraud organization, 34% of insurers refresh their automated red flags/business rules annually.

Predictive modeling can help insurers identify potential fraud with greater accuracy and confidence. Fraud detection models work with many internal and external data sources to send out alerts in real time. Traditional data sources such as CRM, claims database, policy administration systems, etc… are now supplemented with external sources as credit bureau data, social media, etc.

Predictive models are self learning. After the initial training and run, they require minimum handholding except for tuning the models to adapt to newer anti-fraud techniques. These models track nearly hundreds of variables to identify potential fraudulent cases. These variables could range from the customers demographics data and policy information to macro trends of their segment and specific geographic indicators.

When Does Analytics Come into Play?

Insurers large and small have dedicated teams to counter fraud. However, fraudsters have consistently managed to stay ahead of the curve by exploiting some type of loophole within the system. While it may not be possible to address all revenue leakages due to fraud, advanced analytics and predictive modelling can stem the flow to a substantial extent. Analytics models at the time of new policy issuance can help categorize proposals as either low risk or high risk or something in between. The high risk proposals are given additional scrutiny and the low risk proposals can be processed straight through.

Likewise, at the claims stage, the low risk claims can be processed straight through and the high risk claims can be investigated in detail. This optimizes the effort and time of the team and improves the overall customer experience as well.


Interested in how predictive analytics is used to tackle claim fraud? Read our case study Life Claims Fraud Case Study to learn more.

 

Related Posts

How Sentiment Analytics Can Drive Growth for Insurers

Insurers have a near-constant stream of unstructured data at their disposal that can be used to drive growth by improving policyholder retention and identifying cross-sell and upsell opportunities. One of the challenges for insurers is sorting through this mountain of unstructured data quickly to gain an accurate understanding of the sentiment of their customers in real time.

Cheers to Stream-based Data Integration...and to Never Losing a Byte!!

Enterprise applications belong to a vibrant ecosystem and consequently the data they generate is large and varied. Enterprises both benefit and suffer from this nature of application and data.Whenever a new application is to be deployed in an enterprise that integrates with the applications in the ecosystem, the precondition is an 'expansive data definition with referential value' on day 1 to start integration. Traditionally, this approach to data integration involves identifying a target data structure, and force fitting data from all sources into it. This is done to ensure a 'seamless' integration - never mind the loss of data considered irrelevant.

Using Analytics To Counter Early Claim Fraud

According to the Coalition Against Insurance Fraud, at least $80 billion in fraudulent claims are made annually in the U.S across all lines of insurance. This translates more than $400-$700 per year in increased premiums for each American family.