Reduce Insurance Fraud: Why Insurers Need Machine Learning

Fraud detection in insurance is an arms race. Fraudsters are creating new ways to push through fraudulent claims every day, and insurers are trying desperately to identify and block them the instant they crop up. 

According to a 2022 study by The Coalition Against Insurance Fraud (CAIF), insurance fraud costs the P&C insurance industry upwards of $45 billion dollars each year, amounting to about 10 percent of the industry’s total incurred losses and loss adjustment expenses. 

Ten percent of $45 billion is $4.5 billion, which is a huge loss driver that is often under-captured, hurting carriers’ bottom line significantly. 

However, insurers who invest in reducing fraud effectively and improving risk selection and pricing will improve their loss ratio.

How do insurers detect fraud?

Today, one of the most common anti-fraud technologies are rules-based systems.

These catch obvious fraud patterns with a “black and white” logic. The approach isn’t very effective at uncovering new schemes or adapting to emerging fraud patterns. As a result, insurers’ existing fraud programs are vulnerable to types of fraud that aren’t blatantly obvious.

How can insurance companies improve fraud detection?

Some insurance companies understand the limitations of the rules-based approach and are turning to more sophisticated anti-fraud technologies like predictive modeling, link analysis, and artificial intelligence.

But just because a new anti-fraud technology promises to utilize these new tech methodologies doesn’t necessarily mean it’ll do much to save you a large amount of money on fraudulent claims.

You’ll want to carefully evaluate fraud detection techniques and technologies to make the best decision for your company. Generally, though, using machine learning as a standalone solution or a complement to existing rules-based systems is a good idea.

3 things to look for in a machine learning anti-fraud model

1. Make sure the fraud model scales.

Your rules library must expand and adjust as fraud evolves, which means scalability is required.

A rules-based approach can make the system slower and puts a heavy maintenance burden and cost on your team of fraud analysts, requiring lots  of manual reviews.

Machine learning, on the other hand, requires minimal human involvement as the model learns automatically from old and new data.

Machine learning becomes more effective with more data; it’s able to pick out subtle differences and similarities amongst the data, saving your team tons of manual work. Contrast this with rules-based models where the cost of maintaining the fraud detection system multiplies as claims grow, because new rules are constantly needed to keep up with new fraud schemes.

2. Look for expansive detection coverage and accuracy.

Rules-based fraud detection systems run the risk of being too broad and rendering false positives. In turn, this reduces investigator efficiency and leads to a negative customer experience when a smooth, empathetic customer experience is most vital. To prevent this, insurers often change the rules to capture only the claims with the highest probability of fraud, allowing vast numbers of fraudulent claims to slip by.

When special investigative units or claims staff verify and self-report the claims they think need further investigation, machine learning models learn those patterns and get better at predictions over time.

Machine learning fraud systems can be more accurate, more configurable, and easier to improve than rules-based systems, making fraud analysis easier and more effective, especially in keeping up with the latest scams.

3. Look for efficiency optimization.

Implementing if-then rules for a rules-based approach is easy at first, but requires hours of manual work and supervision to maintain. The result is a system that doesn’t adapt well over time, because it can be challenging to find the exact rule you need to change, and repeating this process over and over again is frustrating and time-intensive.

Machine learning can evaluate many claims fast and in real-time because it continuously analyzes and processes new data, from first notice of loss to claims closure.

Moreover, advanced machine learning models like neural networks can autonomously update their models to reflect the latest trends and prior fraud results, enabling a more flexible and automated approach that speeds up fraud management.

Fraud scoring: the next big thing

Unlike a rules-based system, which only produces a yes or no response, machine learning gives a fraud suspicion score from 0 to 1000, like a credit score. It also provides context that allows insurance carriers to take different actions based on their risk tolerance.

Machine learning offers a more personalized and adaptive approach, Letting you pass off less-suspicious claims for faster claims payouts, and flag suspicious claims for further investigation. It empowers special investigative units, claims teams, and other fraud professionals to make more informed decisions faster.

EIS provides insurance carriers with this type of AI/ML technology to combat fraud. Our machine learning models are better at surfacing suspicious claims than the rules-based systems most insurers use today, and have a higher chance of identifying fraud that isn’t as obvious… and is more accurate and scalable.  With our fraud detection technology, a large P&C carrier established a robust fraud analytics program in less than six months that achieved over 200 percent ROI.

If you’re interested in what machine learning could do for your fraud operations… or if you’d like to put some numbers to the money it could save you over time, book a call with one of our in-house experts here.

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