arrow left facebook twitter linkedin medium menu play circle
May 21, 2020 - Tom Shell

Can Machine Learning Combat Fraud in the Insurance Industry?

Insurance is highly susceptible to fraud.

Insurance is highly susceptible to fraud.

Insurance fraud has existed since the dawn of insurance companies. No insurance provider is immune to its effects, particularly as schemes can vary in form, function, and complexity. And when no two fraudulent claims look exactly alike, it can be seemingly impossible to identify repeat issues and develop solutions at scale to target fraud. 

However, given that U.S. providers lost more than $34 billion to fraudulent claims last year alone, it’s an issue that insurance companies can no longer afford to tackle with current fraud detection methods.

In response, many insurance organizations are turning to Big Data to learn more about the varied nature and shared characteristics of fraud. Advancements in machine learning fraud detection are fueling this charge. By leveraging existing data, including unlabeled data points, insurance providers can be better prepared to identify and fight back against policy abusers.

Why Manual Fraud Detection in Insurance is No Longer Enough

Many insurance companies use risk detection software to aid in the fight against fraud, but research suggests that human insight is still critical to the process. Many insurance companies use risk detection software to aid in the fight against fraud alongside manual processes. However, McKinsey notes that manual controls are not very effective and typically cost more than automated solutions. As a result, poor detection increases the risk of developing false positives, which also increases the wasteful spending of precious resources.

There’s a consensus among many companies that involving humans allows for greater insight into fraud patterns. In turn, these insights can help organizations better automate screening rules by teaching their risk detection software key things to look for. 

There are several problems with this logic, however. First and foremost, automation is designed to reduce human involvement along with its associated costs and inconsistencies. By not handing over more authority to automated solutions, companies could be doing themselves a disservice in financial terms and data integrity due to human error. 

Reviewing claims manually is costly and time-consuming, and without comprehensive (and often expensive) training, personnel can overlook potential fraud cases and/or detect false positives. And if a false positive is detected, it can end up pushing away loyal customers and leave a lasting mark on your reputation. 

What’s more, reviewing claims manually can also delay resolution for the client. Customers who are filing legitimate claims may not be able to afford to wait for unnecessarily lengthy review processes.

Introducing Machine Learning Fraud Detection to the Insurance Industry

Improving fraud detection could save insurance companies billions of dollars.

Insurance providers typically turn to fraud detection software to flesh out potential cases of fraud that need further manual review. Typically, these cases are selected based on transactional rules built into the software. When one of these rules is triggered, it sends the case to a human representative for further investigation.

This has long been the traditional approach since the emergence of fraud detection software, yet this, too, has its limitations. Because much of the software is based on logic statements like “If: Then” or other hot-or-cold actions with no in-between, complex fraudulent cases can easily slip through the cracks while false-positive cases become obstructive.

As a result, cases assume a binary labeling approach as either fraudulent or authentic, with little or no context surrounding them.

Combining machine learning fraud detection with the principles of fraud detection software is creating new avenues for insurance companies. Machine learning goes beyond the typical logic statements by including unlabeled data to plug expensive leaks in revenue due to fraudulent claims. 

By definition, machine learning refers to using complex algorithms that are capable of “learning” based on the data they collect over time. These algorithms analyze patterns to look for anomalies and generate insights based on their data sets. In same the way human brains can collect, sort, organize, and analyze data to make inferences, machine learning can make predictions based on past activity but at a faster rate and with the ability to handle much larger data sets.

Machine learning algorithms have successfully been deployed in predictive analytics, spam detection, and personalized product recommendations. Now, the insurance industry at large can also capitalize on its powerful features to reduce human error and false positives.

The Roles of Machine Learning in Insurance Fraud Detection Software

Machine learning fills in gaps left behind by human-driven processes.

As a whole, technology’s role in modern businesses has always been to fill in the gaps created by existing processes, and machine learning fraud detection in insurance is no different. Machines have always been better at crunching large numbers compared to the human brain and can do so in a consistent manner at a relatively low cost. 

And now with a machine’s ability to “learn” and generate deeper, more complex insights, its benefits are being explored in detecting insurance fraud better, faster, and cheaper than human personnel.

Let’s look at machine learning’s role in insurance fraud detection software:

Efficient Processes

Humans are capable of performing repetitive tasks, but the results of each task can vary in terms of time, accuracy, and quality, all of which can lead to inefficiencies in the process. Machines can fill this gap because they’re not susceptible to personal judgments, fatigue, or the potential to overlook or misplace data. And with just one programming (compared to ongoing training for human personnel), machines can better detect patterns or issues in insurance claims.

Scalable Solutions

Machines are capable of handling large sets of data without making additional investments in human resources, workspace requirements, or storage space. In fact, machine learning becomes more efficient the more data you feed it because it has more dots to connect and more insights to offer. 

Faster Results

Ditching time- and money-draining processes in favor of machine learning can help speed up results, which can have a positive effect in many other areas, like customer satisfaction. The fewer false positives created, the faster legitimate claims can be handled and the happier your policyholders will be. Machine learning can facilitate multiple transactions in real-time and drastically reduce or eliminate the need to rely on manual review.

Machine Learning Fraud Detection is the Future of Insurance

Though it sounds more like an ideal scenario of the future, machine learning fraud detection is a solution currently being explored and deployed. Discover how DataVisor is bringing machine learning fraud detection to the insurance industry by downloading Combat Insurance Fraud with Machine Learning. 

about Tom Shell
Tom is a veteran in technology having worked at startups and large enterprises throughout his career. He is excited to be launching DataVisor's global partnership and alliances programs and applying his experience to help bring game-changing solutions to customers around the world. A key part of that effort is the strategic alignment with key partners around the globe to create joint value for customers with DataVisor's technology and solutions.
about Tom Shell
Tom is a veteran in technology having worked at startups and large enterprises throughout his career. He is excited to be launching DataVisor's global partnership and alliances programs and applying his experience to help bring game-changing solutions to customers around the world. A key part of that effort is the strategic alignment with key partners around the globe to create joint value for customers with DataVisor's technology and solutions.