AI vs. insurance fraud?

The U.S. insurance industry is a colossal $2 trillion in market value, with P&C) insurers accounting for nearly half of that market. However, this massive industry is increasingly plagued by fraudulent activities, costing billions of dollars annually.

The Coalition Against Insurance Frauds, an unbiased industry group, estimates that P&C insurers alone lose approximately $45 billion each year to fraudulent schemes. These fraudulent practices not only burden insurers but also impact consumers through higher premiums and taxpayers who ultimately foot the bill for fraud-related losses.

While insurance fraud presents a significant challenge, advancements in artificial intelligence (AI) offer promising solutions. To effectively combat this issue, it's essential to first understand the various types of insurance fraud and the individuals who perpetrate them.

Dissecting insurance fraud in the P&C insurance space

Although insurance fraud can manifest in various forms throughout the entire insurance value chain, this article will concentrate on two particularly vulnerable areas.

First, is fraud that happens before and or during the purchase of a policy? A significant portion of insurance fraud involves policyholders who intentionally underreport their risk profiles to secure lower premiums. This includes instances like misrepresenting flood zone information, home safety features, driving history, or medical history when purchasing home, auto, or liability insurance.

Second vulnerable area, fraud post purchase of policy/ during claims processing. This includes:

Staged or exaggerated incidents:

This includes incidents anywhere between an unnecessary windshield replacement that was reported damaged, to staged auto accidents or even arson by organized insurance fraudster rings.

Significantly higher cost of services covered under insurance policy:

During claims processing, unethical service contractors often overcharge or bill for services that were never rendered. While insurance companies invest heavily in automating claims processing to improve efficiency, it's crucial to implement robust safeguards to prevent fraud. If automations are not carefully designed with adequate oversight, insurers risk significant financial losses.

Internal employee frauds:

Unethical insurance agents have exploited at risk demographics, such as elderly individuals, by collecting premiums through non-standard or non-electronic methods, ultimately failing to provide the promised coverage.

Exploring AI and analytics enabled anti-fraud systems:

AI excels at identifying patterns within large datasets. However, insurance fraud, often characterized by anomalies rather than recurring patterns, presents a unique challenge. Nevertheless, a strategic combination of AI and human expertise has proven effective in mitigating fraud-related revenue loss.
The goal is to proactively identify potential fraud before it occurs, rather than reactively investigating fraudulent claims. By leveraging AI to flag suspicious transactions, insurers can prioritize manual review and intervention, ultimately reducing financial losses.

Here are a few ways AI is being leveraged to identify frauds:

Live behavior analytics during a live voice call with the call center office:

AI models built on top of Natural Language Processing (NLP) systems analyze customers’ behavior through analysis of purchasers tone, articulation and emotions to identify in-genuine conversations
Such conversations, especially during investigations before claims processing, or even during claims processing, can be flagged for an additional manual due-diligence.

Integrating rule based systems with advanced analytics:

Algorithms analyze historical fraud data to uncover hidden relationships between data points, identifying suspicious patterns for investigation. For instance, these systems have successfully identified patterns such as windshield damage claims submitted on specific dates and times, using particular mobile devices, and originating from certain geographic areas, as potential indicators of fraud.

Near real-time verification of information provided through third party data aggregators:

On average, insurance purchasers are known to change their insurer once in every 18 months. This implies that significant demographic data, history of the consumer, earnings data, history of assets being insured, etc have been provided at some point in the past.
Securely aggregating customer data across insurers to validate information provided in real-time, reducing misrepresentation and fraud would benefit all.

DocumentAI based systems to analyze cost of services from contractors covered under policy benefits:

GenAI based document analysis systems are now significantly better at understanding and analyzing invoice documents, service completion documents, police reports, incident reports, etc regardless of their non-standardized formats.
There is enough historical data available to compare the type of incident being covered against cost of service provided by a covered contractor, adjusted based on geographical variation in expenses.
As soon as a claim is submitted, a well-trained AI model can quickly flag inflated claims for further manual verification.

AI- powered verification for vulnerable groups:

Fraud prone demographic groups, such as the elderly, or those based in more remote areas of the country, can be protected through LLMs based AI Agents. Those agents can confirm expected policy benefits and policy premiums through automated call systems if the policies are purchased through insurance agents
While there are very few bad actors, such systems will help preserve the overall agent ecosystem.

The fight against insurance fraud requires a multi-pronged approach that combines cutting-edge technology with human expertise. While this article has explored the power of AI in proactive fraud detection, the potential of AI to accelerate investigations and empower human agents cannot be overstated.

As generative AI continues to evolve, the insurance industry must foster collaboration between humans and machines to create a robust and adaptable defense against fraud.

Deepankar Mathur leads Searce's North America Consulting business dedicated towards high growth venture backed Startups in the region. In the last several years, he has strategically advised over 500 startups across technology initiatives, go-to-market planning and business models to accelerate the path to revenue & profitability.

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