Canadian Underwriter
Feature

Spotlighting Underwriting Fraud


November 1, 2013   by Wes Gill, Executive Lead, Enterprise Risk Manager, SAS Canada


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Wes Gill, Executive Lead, Enterprise Risk Manager, SAS Canada

Fraud is a major issue for all insurance companies. Although measuring fraud precisely continues to be difficult, the Insurance Bureau of Canada estimates the cost of insurance fraud to the Canadian economy to be more than a billion dollars a year. What’s more, the insurance industry estimates that 15% of what consumers pay for insurance ends up covering fraudulent insurance claims alone.

To combat this issue, the current focus of most insurance companies has been on detecting and preventing claims fraud, yet a significant amount of insurance fraud is associated with underwriting fraud.

Underwriting fraud occurs when someone intentionally conceals or misrepresents information at any stage of the policy life cycle when obtaining insurance coverage. It affects most lines of business, especially commercial auto insurance, workers’ compensation, property and even life insurance. As perpetrators of fraud are becoming more sophisticated, the need to be proactive and identify fraud before the policy is issued as part of the underwriting process has never been more critical. Yet the call for a greater focus on underwriting fraud still lags when compared to other types of fraud.

A CLOSER LOOK AT UNDERWRITING FRAUD

Application fraud

Insurance carriers are competing for business like never before, with consumers demanding more options, such as the ability to get quotes, buy and manage policies directly, including via mobile devices. Competition and mobility have resulted in insurance companies implementing “straight-through underwriting processing” projects that limit the amount of due diligence undertaken before a policy is written.

Savvy fraudsters are sitting on the sidelines, ready to take advantage of this vulnerability, clearly aware that insurance companies are under pressure to increase premium revenue and, as such, do not always check all the application details for accuracy or inconsistencies.

Application fraud runs the gamut – applicants can withhold personal information such as social insurance numbers, maiden names or prior addresses to prevent effective searching for previous claims or credit histories. Additionally, everyday consumers are becoming much savvier in their understanding of how to successfully cheat when applying for insurance, as more and more applicants misrepresent details to reduce rates.

Many people think that insurance fraud is a victimless crime, but the reality is that consumers are victims. Insurance fraud has a direct impact on the amount everyone pays for health, auto, homeowner’s and life insurance.

Typical rate falsification techniques include the following:

• fronting – where a parent states that he or she is the primary driver of a vehicle, instead of the child, to reduce the premium;

• garage flipping – changing the address where the vehicle is most used, such as someone with two homes using a rural address instead of a city address, where the car is actually stored and used; and

• manipulation – changing rating factors such as miles driven, the age of the primary driver and even education levels to reduce the premium.

To address the problems with application fraud, insurance carriers are using application pre-fill tools and analytics to model fraudulent behaviour. Through application pre-fill technology, with as little information as a telephone number, policy-level data can be populated. This includes location, coverage limits and deductibles, current in-force details and payment and lapse information.

By integrating these pre-fill technologies with real-time, anti-fraud analytics tools, insurers can track changes in online application data. If certain rules or combinations of rules are triggered, then the carriers are alerted and can intervene immediately to potentially deny the policy.

A little white lie: rate evasion

Rate evasion or data misrepresentation is the most widespread form of underwriting fraud. It is also the most undetected. Whether it is an undisclosed driver on a car insurance application or not reporting a history of smoking for life insurance, data misrepresentation is defined as deliberate hiding or falsification of a material fact.

To deter rate evasion, it is critical to address the issue in real time throughout the quotation process. Insurers must ensure that information is correct at the point of sale and continue to update information through the life of the policy. Accurate information and data is key, as it is the basis for finding transactions that indicate rate evasion.

To combat rate evasion, insurance companies are using advanced analytics to create a premium leakage predictive model that instantly scores applications for relative risk to predict the likelihood of fraud. Many have implemented an enterprise data warehouse across all lines of business in order to aggregate data and potentially find interrelated information and transactions 

Truth in telematics

As the popularity and use of telematics grows, it is expected that the industry should see a fall in instances of rate evasion in auto insurance. Customer data provided through these devices are turning car insurance bills from a static monthly figure into something variable – the less safe the customer’s driving habits, the higher the cost.

Data from these wireless devices will provide a strong platform to help eliminate misrepresentation – providing insurers with real data on annual mileage, vehicle location, etc. 

Beware of the ghost broker

Another emerging trend, known as ghost brokering, is also gaining in popularity. A ghost broker will offer significantly cheaper insurance rates than a legitimate insurance broker by changing key details of the policy to ensure the insured pays lower premiums.

More often, the ghost broker applies for genuine insurance and alters specific details, changing anything that might have a negative impact on the quote, including residency status or claims history. This leaves the customer with an invalid policy.

In some cases, the broker takes out a policy and then cancels it once the insurance certificate has been issued, leaving the customer uninsured and holding a policy that is not worth the paper on which it is written.

These ghost brokers operate through small ads or websites and offer cheap insurance. They often target people who may think it is hard to get insurance, such as those on tight budgets, like students, or those whose native language is not English or French.

Data analytics is used to look at triggers or characteristics of an application that suggest ghost brokering is taking place. It can be done by deploying statistical analysis and cross referencing information, such as date of birth, driver’s licence information and past histories of activities with industry databases to look for misrepresentation.

Spotting red flags

Traditional underwriting operates using rules-based models that produce various “red flags” during the underwriting process to indicate the need for additional research or follow-up. This model, for the most part, relies on the judgment and expertise of the underwriters alone.

Couple this with the fact that insurers are being forced to cut expenses, perhaps leaving little time to check application details for completeness and accuracy, and that it is a fiercely competitive market where a consumer’s main criteria for choosing an insurer is often based on price. The net result is an environment ripe for underwriting fraud.

To ensure rating integrity and to prevent premium leakage, insurers need to implement analytics technologies and conduct sophisticated data analysis in real time. Precise detection can only be realized through the creation of an overall picture of the probability of fraud so that action can be taken during the underwriting process.

As today’s insurers look to develop new strategies for employ
ing advanced technology to detect and prevent the various types of underwriting fraud, capturing customer data in real time, using predictive modelling, creating special investigative units and integrating insurance data warehouses need to be among the techniques deployed.