When insurers get asked the question: What is one of the biggest challenges your industry is currently facing? The most common answer echoed across different insurance sectors is a two-word answer: Claims Fraud. 

Claims fraud is a pervasive issue across the industry. The extent of fraud between countries and between insurance sectors varies. However, across European countries, it is estimated that detected and undetected fraud accounts for up to an astonishing 10% of claims expenditure. While in the US It is believed that insurance fraud accounts for up to $308.6 billion in losses per year. 

What makes claims fraud most challenging is that it is often very difficult to detect and prove without costly investigations. There is no question that insurance providers are acutely aware of the claims management fraud problem, but are often unable to detect it due to a lack of resources.

However, today there is hope. And, that hope comes in the form of sophisticated AI technologies. 

Artificial intelligence is set to revolutionize the insurance industry as we know it. According to McKinsey, by 2030, AI is predicted to increase productivity and reduce operating expenses by as much as 40% across insurance functions.

Today, innovative insurance agencies are looking to these technologies, to advance claims automations and detect fraud with unparalleled success.

In this article, we take a look at the challenges that insurers are facing today with fraud detection and explore the different ways in which artificial intelligence can be used to combat fraud in the insurance industry.

The problem with insurance fraud 

There can be no doubt about fraud’s immense cost. This cost comes both to the insurance provider and innocent insurance customers, who are stricken with increased premiums implemented to offset costs associated with fraudulent behavior. 

Insurance fraud can come in numerous forms. Claimants can falsely claim injuries or falsify damage to vehicles or property. When making an application they may submit false documents, falsify medical records or receipts, or make false statements. The potential for fraud is endless.

Despite spending a lot of time and energy addressing fraud, why does fraud detection remain such a bottleneck for insurers?

The sophistication of claims fraudsters today makes distinguishing fraudulent from non-fraudulent claims increasingly difficult.

Further complicating the issue is an outdated process for claims management. Conventional claims management requires claim handlers to manually manage a multitude of documents. It is common for key information to be overlooked during this process. In many cases, claims are evaluated according to the level of expertise of the claims handlers. As a result, claims are judged on a subjective level rather than an objective one. 

Within this process fraud often goes undetected for a number of reasons: 

• Historical fraud is not identified correctly which makes predicting instances of future fraud more difficult
• Insurance company’s fraud detection methods change regularly, which makes it more challenging to identify fraud based on historical data
• There is an inability to assess and make correlations between suspicious relationships and scenarios accurately
• There are not enough resources available to investigate every suspicious claim quickly and effectively 

How AI is addressing insurance fraud 

The insurance sector is one of the most heavily regulated industries globally. As a result, adopting new technologies and innovations has often been a huge challenge for insurers.

However, in recent years the insurance industry has become increasingly eager to adopt new technologies that can improve claims operations and detect fraud more easily. Today it is reported that 87% of insurance carriers invest more than $5m in AI-related technologies every year.

The use of artificial intelligence can help prevent fraud by monitoring and analyzing customer behavior, inspecting documents, and flagging suspicious activity. A major advantage of AI fraud models is that they become more accurate as more data is fed into them. A model’s performance over time improves, allowing for higher automation, and becoming more efficient, thereby helping insurers improve their bottom-line.

AI technologies are being utilized in several ways to address fraud: 

Extracting data efficiently with NLP techniques 

NLP stands for natural language processing, which is a set of techniques that can process texts and convert them into structured data. NLP is used to extract information from texts so that machines can process insights. Information such as a person’s demographics and health status, along with other more detailed data, can be extracted by using this technique.

It is possible for insurers to rapidly process large volumes of data and extract pertinent information using NLP. This allows them to gain access to key data, which is then fed into analytics models to detect fraud more effectively.

Defining suspicious scores

As data is extracted from NLP systems it is fed into AI fraud detection systems. 

Fraud prediction systems then evaluate all the data collected on claims. Claims are benchmarked and evaluated against a library of suspicious scenarios. As suspicious patterns are detected, claims are then given a suspicious score that determines the degree to which a claim is deemed to be fraudulent. 

This process is quick and highly accurate. Suspicious claims are immediately flagged for investigation while non-suspicious claims paid out immediately. 

Uncovering insights and make connections 

Causality and link detection systems help insurers understand the root cause of fraudulent claims. As a result of these systems, graphical insights are provided, hidden connections are revealed, and network relational correlations are discovered for efficient claims management to allow handlers to more accurately investigate claims.

In addition to allowing insurers to see centralized information on all claims, they also enable them to cross-reference claims in order to reduce errors.

Predicting future fraud 

Predictive analytics models are utilized for future fraud prevention. These models help to identify the high-risk potential customers who are more likely to commit insurance fraud. 

Models of this type are extremely useful for insurers, as they allow them to detect fraud before it takes place. This allows insurers to make more accurate assessments of customers that are applying to join their agencies. 

Conclusion 

During the past few years, AI has become more accurate and efficient at detecting insurance fraud. Recent technological advancements in deep learning and natural language processing have contributed to this development.

At umAI we have seen first hand the benefits insurers get by utilizing AI technologies to address fraudulent claims. Take for instance a leading Israeli insurer client of ours that saw a 1.5% reduction in unnecessary and fraudulent payouts, when implementing our Human-Machine AI technology. This reduction in payouts represented millions of dollars in savings in the first year of implementation alone for the company. 

For insurers the benefits of implementing AI into claims operations are endless. From delivering on ROI to increasing customer satisfaction, AI has the potential to radically transform fraud detection processes.

Looking to adopt AI into your organization to help address insurance fraud, get in contact with us today.