How Predictive Analytics in Insurance will Benefit the Industry in 2020
It’s hard to remember a time in the insurance industry when we didn’t use computers. Over the years, insurers have used software, machine learning, artificial intelligence (AI), and predictive analytics to reduce human error, issues, and expenses, as well as increase productivity, sales, and profits.
What is Predictive Analytics?
Predictive analytics is a category of data analytics used to make intelligent predictions about future outcomes based on internal and external historical data and analytics techniques. Predictive analytics tools and models help insurance organizations use past and current data to forecast future trends and behaviors of insureds.
Predictive analytics has captured the support of a wide range of organizations (not just insurance), with a global market projected to reach about $10.95 billion by 2022, according to a 2017 report issued by Zion Market Research.
So how will predictive analytics affect the insurance world in 2020? Check out these three ways predictive analytics will help the industry in the year to come.
1. Streamline the Claims Process
With predictive analytics models and reporting, adjusters can analyze their historical claims processes and make informed decisions to increase efficiency and productivity. Organizations use historical claim data to determine factors that could affect the outcome of current claims. This, in turn, streamlines the process, helping them close claims that before, took weeks or months to do.
Companies like Lemonade are starting with an AI/behavioral-first approach. They use a chatbot to process claims faster and provide customers with faster payouts. These chatbots review the claim, verify policy details, and send it through a fraud detection workflow before instructing the bank to pay for the claim settlement. This, in turn, speeds up the process, frees up insurers to work on more high-risk claims, and reduce human error.
2. Save Time, Money & Expenses
Predictive analytics systems help adjusters prioritize claims to save time, money, and resources. The platform predicts high risk, high cost, or problematic claims early in the claim lifecycle, allowing the adjuster to put in place procedures to drive a better claim outcome for the injured claimant and a better financial outcome. The predictive models help adjusters set reserves better, administer claims better, and get the claimant back to work at a faster rate.
All data, images, videos, or field notes upload into a predictive analytics system, allowing the adjuster to read through years of entries and notes that were impossible for humans to sit and read through before. They can filter by keywords or phrases, rather than sifting through years of notes to find claim trends, issues, or outcomes.
3. Identify & Prevent Fraud
According to the FBI, the annual losses related to insurance fraud are as high as $40 billion, costing the average American family $400-$700 in increased premiums each year.
To combat this, insurers are using predictive analytics to find and prevent potential fraud before it happens. Many turn to social media or online searches for signs of fraudulent behavior and using that data for future claims to watch online activity for red flags.
Many have discussed using voice analysis devices to record claim statements or the first call in to report the injury to detect within the human voice whether they’re supplying a false analysis. This happens through truth analysis solutions that detect intent within the tone.
The Bottom Line:
Going forward, more insurers will use predictive analytics and AI to help forecast events and gain actionable insights into all aspects of their businesses. Using analytics, insurers can gain a competitive advantage that saves them time, money, and resources, while helping them plan for future outcomes.
Organizations can also cut the total number of claims by using a solution like SmartCompliance, a certificate of insurance tracking solution, to be sure proper risk transfer and subrogation are in place.