What is Predictive Analytics and What Does it Mean for Insurers?
If you’ve found yourself here, you’ve likely asked, what is predictive analytics?
In its simplest form, predictive analytics is a decision-making tool that uses statistic-driven models to predict user behavior. We’re seeing it gain momentum in the insurance industry because it can tell us if a claim is real, agents are honest, and applications are profitable. So far, the impact we’ve seen for insurers is significant, so we’re not surprised by the momentum. In this article, we’re digging deeper into what predictive analytics is and why it matters for insurers.
Skip to the Key Takeaways
- What is predictive analytics?
- Why is it important in insurance?
- How does it work (with data collection, analysis, and modeling?)
- How to use predictive analytics in insurance
There are three key elements that go into predictive formulas to uncover historical data patterns and predict what comes next. These elements include descriptive, diagnostic, and predictive. Here’s the breakdown.
- Descriptive analytics asks what happened
- Diagnostic analytics identities why it happened
- Predictive analytics ask what might happen next because of it
Predictive analytics at its core is a decision-making tool. Companies like ForMotiv integrate insurance systems with statistic-based algorithms and automated machine learning to identify and predict user behavior. Using this kind of advanced analytic software, insurance companies can increase their bottom line by improving operations and reducing risk and fraud.
It’s exploding with popularity and demand right now, but predictive analytics is nothing new. In fact, it’s been around for a long time — we’re talking ancient pyramids of Egypt long time! Historians believe the Egyptian labor planners used one of the first models of predictive analytics to forecast the resources and time needed to build the pyramids. (Spoiler alert: it was a lot of both. A single pyramid took about twenty years to build.)
In the 17th century, predictive analytics began modernizing into how we understand it today. Investors and manufacturers used it to calculate the cost of doing business and predict the loss of goods during shipping voyages.
Insurance companies made their big debut with predictive analytics when Edward Lloyd used a model version to predict levels of risk for potential shipping client payouts in 1689. After he found investors willing to sign off on a statement of risk, Lloyd developed insurance policies to benefit shipping companies and their investors. Since then, predictive risk models have become essential for every insurer.
When Predictive Analytics Met Artificial Intelligence
When computers came into the picture, the power of predictive analytics exploded. The manual calculation was no longer necessary — now, computers could calculate huge amounts of data (almost) instantly. And when calculation became smarter, so did the predictive capabilities.
Computerized analytics were largely used for military operations at first. It didn’t start reaching other industries until the 1950s. Flash forward to today, and every major industry uses predictive analytics as an integral part of doing business.
Along with the advancement of computers came artificial intelligence and machine learning, and as a result, the landscape of predictive analytics also advanced. No longer reserved for expert mathematicians and statisticians, AI software gives data knowledge to entire teams within insurance companies.
Now, there are a ton of AI companies combining predictive analytics with artificial intelligence to predict user intent. To take this intelligence a step further, companies like ForMotiv not only predict user intent, they can also react in real-time. History is still writing this cutting-edge software advancement, but so far, it’s captured over 500 billion data points with 92% predictive model accuracy. Want to see it for yourself?
Major industries rely on predictive analytics to solve complex problems, including oil, gas, government, financial institutions, healthcare, and retail. There’s plenty of information available about the impacts on these industries. But what is predictive analytics’ role in insurance’s revenue pipeline?
Predictive analytics can directly impact every sector of insurance, from onboarding to payouts, and all the critical steps in between. Here are a few of the main ways.
Detecting fraud. Agents gaming the system, fraudulent claims, hard fraud, soft fraud, and bots are all growing problems in insurance. (A twenty billion-dollar problem, to be exact.) Predictive analytics powered by AI uses behavioral data points and dozens of predictive models to understand their users in ways that were previously impossible, making it possible to predict fraudulent behavior in your systems.
Predicting risk. Outdated predictive analytics models rely on credit scores, utility bills, financial history, and social media data to forecast risk. But if that worked, we wouldn’t see billions of dollars down the drain every year due to fraud from miscalculated risk. We have to do better; doing business on the modern ‘faceless’ internet requires staying ahead of the changes. With smarter predictive models powered by AI, insurers have the ability to instantly predict if a user is a liability, a high-risk customer, low intent searcher, or displaying bot activity. As a result, upwards of 90% of leads and applicants become higher-quality.
Straight-through underwriting. Smarter risk assessments mean smarter underwriting. AI analyzes thousands of data points from different systems and weeds out the most important information. Insurers can use this to paint clearer pictures of customer risk profiles and tailor pricing to the individual. AI and predictive analytics working together help underwriters reduce bias, human error, and ultimately boost the insurer’s bottom line.
Improve the customer experiences. Insurers’ success is determined by who can offer convenient, customer-centric service when and where the customer wants it. Basic predictive analysis can predict customer purchasing habits, responses, and follow-up purchases for cross-selling. When powered with AI, predictive analysis can also help create a seamless experience that increases customer satisfaction, Net Promoter Scores, and Lifetime Value.
Optimizes operations. Insurance companies can use predictive models to manage resources, set pricing, predict client retention, predict turnover, and predict revenue and expenses. It can also track employee effectiveness to identify patterns for areas of improvement.
Now that we’ve answered what is predictive analytics, it’s time to dig into how it works. Insurers can access its impact in three steps: data collection, data analysis, and modeling.
What do we mean by micro-expressions? Consider all of the behaviors you’re displaying while you interact right now with this content: basic things such as your typing speed, number of keystrokes, mouse movements, time spent on a page to more advanced behaviors like hesitations, cognitive load, familiarity, etc. These are all microexpressions you and every user leave behind like a trail of data breadcrumbs that’s considered the “digital body language.”
To pack the most punch with predictive analytics, you want software that can read “digital body language” and translate it into intent data to precisely predict user intent. For instance, if they’re a genuine, high-quality lead, confused, or risky, low-intent customers.
Formulas that generate advanced analysis like this are not ‘one size fits all.’ Instead, the companies like ForMotiv work with the companies’ data teams to integrate a system based on the insurer’s unique objectives.
Modeling (Supervised vs. Unsupervised machine learning)
Predictive modeling is one of the final steps that analyze both real-time and previous data to project expected outcomes. But there are two important distinctions here: supervised machine learning vs. unsupervised machine learning.
Supervised machine learning datasets are programmed to “supervise” algorithms, learn from their data, and predict outcomes with increasing accuracy. Basically, it’s designed to become more intelligent.
On the other hand, unsupervised machine learning can identify hidden patterns in data without intervention. While this approach tends to take longer to train and implement into a system, it can provide impact results. There is a benefit to both kinds of machine learning in predictive modeling depending on the insurer’s goals.
Insurers have used predictive analytics to predict fraud, improve systems, and increase their bottom line for hundreds of years. And luckily, it’s gotten a lot better since its humble beginning in ancient Egypt. Even in the last few years, it’s grown exponentially — 90% of the world’s data has been collected in the past two years alone.
ForMotiv’s groundbreaking predictive analytic and behavioral intelligence software is helping insurers make big waves in their industry. Here’s how insurers use ForMotiv’s predictive analytics to solve problems:
- Identify high-risk customer behavior
- Predict cases of application misrepresentation
- Increase opportunities for accelerated underwriting
You can learn more about their case studies right here.
By combining big data and behavioral analytics, predicting risk and quantifying losses is a much simpler process and turns your website into your greatest salesperson.
Don’t get left behind with predictive models the ancient Egyptians used. See what ForMotiv can do for you.[/vc_column_text][/vc_column][/vc_row]