Even with today’s many fintech advancements, fraudulent insurance claims cost insurance companies over $80 billion each year in the United States. To offset fraudulent claims costs, insurance companies increase premiums for customers. Insurance fraud detection AI is the key to finally reducing the frequency and cost of fraudulent claims for insurance companies and, ultimately, their customers.

Insurance firms are protecting themselves by utilizing artificial intelligence (AI) algorithms to detect fraudulent or unusual claims. For an industry that has been slow to adopt emerging technologies, AI is becoming increasingly common among insurance companies for its ability to continuously monitor potentially fraudulent activity and automate other tasks, including claims management.

For example, an Accenture study revealed that the proper application of insurance AI for process automation could help insurers save more than $7billion in just 18-months. When combined with fraud detection AI, the potential savings are staggering.

What is Insurance Fraud?

Insurance fraud is the exploitation of insurance policies for financial gain. Unfortunately, insurance fraud is not carried out by consumers alone—insurance agents can also be offenders.

Fraud is common in all insurance sectors, including medical, auto, and home. Today’s most prominent insurance companies are acutely aware that insurance fraud occurs but they don’t always have the necessary resources to detect and investigate all potentially fraudulent claims.

Among the various types of insurance fraud, claims fraud is the most common. Whether it’s an individual making an exaggerated claim or an organized scheme among many to exploit insurance companies, firms that manually file through claims are often left unequipped as fraudsters’ tactics become more sophisticated and firms lack the technological capabilities to keep up. Although insurance claims fraud is not a new phenomenon, it’s historically been difficult to solve.

Types of Insurance Claims Fraud

There are various methods for consumers to facilitate insurance fraud. The most common types of fraud include:

  • Exaggerated claims: The insured person inflates the value of their claim. For example, someone claims the stereo stolen from their home was worth $1000 when it was worth a fraction of that amount.
  • False claims: The insured person makes a fraudulent insurance claim about an incident that never occurred, such as an accident or injury.
  • Duplicate claims: The insured person or provider submits more than one claim from the same provider for the same service date and service.

Detecting Insurance Claims Fraud

Given the strict regulations and nature of the insurance industry, insurance companies have been late adopters of new technology that could save them millions of dollars and hours of manual labor. Traditionally, firms have had to rely on claims agents to manually filter through the thousands of claims made each day to detect any questionable activity or flag if it seems potentially fraudulent. This method is neither practical nor efficient, so a large portion of insurance claims fraud goes undetected—hence the constantly rising cost of insurance premiums.

To underscore the inefficiency of manual sorting, IBM recently noted that up to 30 percent of knowledge workers’ time is spent searching for the information and data they need to do their job. Without automation, there isn’t an effective way to detect trends and patterns among the thousands of lawsuits filed each day for insurance claims fraud.

How AI is Transforming Insurance Claims Fraud Detection

With much of the financial services industry experiencing a technology overhaul, insurance firms are beginning to utilize AI to help process claims quickly and efficiently. AI in claims fraud detection proves highly beneficial, improving customer satisfaction and saving firms valuable resources.

Machine learning and AI algorithms can easily detect patterns in the millions of insurance claims that firms receive each year, allowing them to see outliers and questionable requests in real-time. Among the many uses of machine learning in insurance companies, claims fraud detection AI is a groundbreaking tool helping firms modernize, increase efficiency, and lower overhead costs. Here are four ways AI is being used:

1. Predictive Analytics for Insurance Fraud Prevention

The first defense against insurance claims fraud lies in predictive analytics for early detection and fraud prevention. With each new customer touch-point and further information gathering, predictive analytics can assess the fraud risk of the policyholder and provide early detection for potentially fraudulent activity based on their profile and behavior patterns.

With AI in claims fraud detection, the algorithms provide an accurate claim score and reason code for each claim made, which can help determine if the request is suspicious and requires further investigation. By alerting staff about possible fraudulent claims before processing and pay-outs, insurance firms can better allocate resources to investigate claims that have been flagged as potentially fraudulent, saving them time and money.

2. Using NLP to Analyze Historical Data

A significant benefit of integrating AI and machine learning into the claims filing process is natural language processing (NLP). In addition to processing mountains of information around the clock, NLP can analyze historical data of fraudulent claims and the individual policyholder’s past claims and behavior by assessing recorded conversations and other textual data types, such as emails.

Without using AI in claims fraud detection, this would be inefficient or even impossible to replicate with human workers alone. By monitoring historical trends in a person’s claim history, the algorithms understand the individual’s claims history and if a particular request seems ordinary or suspicious. Automating this process frees up workers for other activities and improves customer experiences with quicker response times and better-informed customer service.

3. Advanced Text Analytics and Data Mining

A common misconception is that AI and machine learning algorithms can only analyze quantitative and numerical data and disregard any unstructured information. AI technologies can give concrete and actionable insights from textual and unstructured information like claims applications, adjuster notes, social media searches, etc.

With these advanced capabilities, AI is helping to streamline the overall insurance claims process and help firms access smarter fraud detection without added labor or costs. AI in claims fraud detection allows firms to quickly analyze both structured and unstructured data from internal and external sources, providing better analytics and protection for the firm. The more information on policyholders that can be accessed and analyzed, the better firms will understand their risk exposure to insurance claims fraud.

4. Real-time notifications

Every day, several fraudulent claims are made amidst the thousands of other legitimate claims filed. Identifying this constant flow of fraudulent claims proves challenging for the staff tasked with manually assessing each new suit while trying to identify unusual patterns or questionable requests. With AI systems running around the clock and constantly monitoring the habits and behaviors of claim applications and policyholders, the algorithms can easily flag potentially fraudulent activity and provide real-time alerts to the firm when a claim requires further investigation.

The earlier insurance firms can be alerted to potentially fraudulent activity, the better protected they are from paying out the requested amount and incurring the related loss. Improving firms’ resource efficiency, AI in claims fraud detection is a helpful tool that can save insurance companies millions of dollars each year.

With better early detection for fraud risk, NLP to analyze historical claims data, advanced data mining, and real-time alerts, insurance firms can leverage AI and better protect themselves against claims fraud and the resulting losses.

Using No-Code AI in Insurance Claims Fraud Detection

AI will continue to revolutionize and transform the insurance industry as more firms realize the benefits of implementing machine learning and AI algorithms into their operations. For insurance firms that don’t have the in-house data science experts and the ability to implement AI systems into their current processes, a no-code AI solution, like Accern, can be a valuable tool. Insurance companies can gain valuable insights through early fraud detection and automated claims processing.

While the insurance industry begins to embrace recent technological advancements in a way it hasn’t before, AI and ML use in these firms will continue to rise. Saving firms valuable time and money, the sub-specialty of NLP is helping firms extract meaningful insights from text data and operate around the clock to provide real-time updates.

With a no-code NLP solution, like the one offered by Accern, underwriting departments, claims departments, call centers and data science teams can more easily test these ready-made models without the need for coding. They can then work with IT to quickly integrate them into their daily processes, current workflows and technology stacks.

Do you want to see how your department can reap the benefits of these powerful AI models?

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