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A slew of digital innovations has upended the insurance industry like no other, with insurers everywhere rushing to adapt to a digital age that’s proving to be as full of complexity as it is of profitable potential. Most insurance companies have welcomed the onslaught of digital advancements which have disrupted their sector, largely because it helps them cater more efficiently to a broader audience of customers.
One such way that insurance companies are harnessing the power of recent innovations is by using predictive analytics to combat insurance fraud. Here’s a breakdown of the power of predictive analysis and how data-savvy software and people are finding fraudsters before they strike.
Fraud is no laughing matter
Insurance fraud is obviously a serious issue, yet few people understand the extent to which fraud has beset the insurance industry and led to crippling economic problems across the country. According to the FBI’s page on insurance fraud, for instance, the total cost of non-health insurance fraud every year in the United States is a monstrous $40 billion, demonstrating why it’s so imperative that insurers are constantly working towards new and more effective fraud-fighting measures.
With the average American family being forced to pay an extra $400 to 700 per year thanks to the insidious impact of insurance fraud, it stands to reason that more accurate methods of detecting fraud would be a huge boon to society. One of the most effective tools thus far discovered in the war against fraud is predictive analytics, or the ability of software and humans to come together, crunch the numbers, and determine if embezzlement or similar fraud is occurring.
Major companies that deal with plenty of transactions each day have long grown familiar with predictive analytics, which enables them to reduce the number of human workers they employ when combating fraud. PayPal has been harnessing the power of advanced predictive data analysis to preserve its branding, for instance, while simultaneously freeing up its human employees to focus on other key areas of importance for the company. When it comes to the insurance industry, predictive analytics is being used to a greater extent than ever before, including in auto insurance.
Medical insurance fraud is on its way out the door
With the American healthcare industry beset by greater demand right now than ever before in its history, it should be of little surprise that medical insurance fraud is severely hampering the ability of medical and insurance professionals to save lives by delivering their promised services. Those who thought that the American economy losing $40 billion a year to non-healthcare fraud will be stunned to know that, according to data compiled by the Blue Care Network, health insurance fraud in and of itself cost a whopping $68 billion annually.
Insurers are finding it difficult to hire the number of investigators needed to manage such a massive amount of medical insurance fraud. These days, however, they’re finding their prayers answered by the rise of predictive analysis software which is capable of crunching huge sums of numbers to determine where and why things don’t add up. Hospitals around the country are digitizing their operations to an unprecedented extent, relying on mathematical algorithms to generate risk scores for patients to determine who’s likely to be conducting fraud.
An important part of this is data visualization, as many of the healthcare and insurance professionals who rely on such analytics to combat insurance fraud aren’t necessarily AI or data experts who can understand huge sums of data. The most notable examples of hospitals leveraging predictive analysis in their IT operations almost universally include mathematical algorithms which are made more understandable via graph databases which healthcare professionals can survey.
As the population continues to age and demand additional healthcare services, mathematical algorithms capable of deducing insurance fraud before it actually occurs will grow increasingly important to the insurance industry. These analytical processes are often heavily automated, meaning human investigators are being freed up at an unprecedented pace to focus more of their expertise on special cases of fraud that are harder for machines to detect. Regardless of the changes coming to the insurance industry in the near-future, it’s indisputable that predictive analytics will continue to be imperative for the successful elimination of insurance fraud.
How Predictive Analytics Combats Insurance Fraud was originally published in Hacker Noon on Medium, where people are continuing the conversation by highlighting and responding to this story.
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