3 ways insurers are using predictive analytics to grow their business

No one can predict the future, but the business model of insurance has always been about making the best-educated guesses possible. Insurers’ bread and butter depends on accurately assessing the risk level of prospective clients and adjusting their eligibility, premium rates and coverage allowances accordingly. In other words, the success of an insurance business depends on, essentially, a bet — so making predictions based on analysis has always been a key strategy in the industry.

Fortunately, technology has come a long way since the most primitive concept of insurance showed up in the Code of Hammurabi around 1750 B.C.E. These days, predictive analytics is an entire field of study with an evolving suite of specifically designed computer technology. While the future is opaque even to the smartest of artificial intelligences (AI), here’s how machine learning, AI and other predictive analytics tools are changing the future of insurance — and making it better.

1. Helping triage and process claims

The claims process can be one of the most work- and time-intensive parts of any insurance agency’s business. When a client makes a claim, they likely want to receive their benefits quickly and painlessly — while it’s the insurer’s obligation to verify details and keep costs as low as possible. These two goals are often in opposition, but predictive analytics can help bridge the gap.

For example, predictive analytics can help insurance companies determine how to prioritize claims in ways that can help reduce insurer costs while increasing customer satisfaction, using in-depth analyses of historic patterns. The same technologies can supply insurers with real-time data that can reduce the time and effort it takes to process those claims once they’re triaged. In short: It’s a win-win situation.

2. Sniffing out customers who may cancel — and reaching out for retention

As critical as risk reduction and client qualification are to the success of an insurance agency, retention is just as important. It doesn’t matter how accurately a client’s risk level and costs are calculated if that client ends up canceling their policy.

Fortunately, predictive analytics can help with this problem, too. By utilizing complex behavioral and historical data to analyze and identify the warning signs that a client is about to pull the plug on their policy, predictive analytics gives insurers the opportunity to reach out ahead of time to discover what that client needs and find ways to strengthen the relationship. That can translate to higher retention rates — and a more substantial bottom line.

3. Increasing the efficacy of fraud prevention

Using predictive analytics to mitigate risk up front by assessing potential clients’ behavioral data is one thing — and an important thing, at that. But predictive analytics technologies can also be used to more easily and efficiently sniff out fraudulent claims, a problem that increases costs on both sides of the equation. According to the Coalition Against Insurance Fraud, insurance fraud costs the industry an estimated $308.6 billion per year; meanwhile, the FBI estimates such claims cost families $400 to $700 yearly in unnecessarily increased premiums.

Enter predictive analytics, which can be used to help detect fraudulent claims more quickly by analyzing real-time data and raise red flags that might only look pink — or even white — to human eyes.

For instance, some insurers are using behavioral biometrics to verify users and establish trust. Behavioral biometrics analyzes how users physically interact with a website or application. By carefully monitoring how users interact with the platform, insurers can detect any anomalies that might indicate they are trying to commit fraud.

According to Payoda, a company that offers predictive analytics services to companies including insurance agencies, one leading firm reduces its property and casualty claims fraud by 23% using the technology.

As AI and machine learning continue to evolve and push the boundaries of how predictive analytics can serve the insurance field — and other industries — insurers, too, will respond with their own innovative ways to use these tools. And in making the insurance industry and its processes more efficient and accurate, these technologies stand to reduce costs and friction not only for insurers but also their clients — an outcome well worth the investment.