Analytics is a big discipline that leverages existing information to make predictions about the future. It has revolutionized how companies mine and extract actionable insights about customer behavior. In insurance, it can help insurance professionals identify important clues to take business action.

So, how can Predictive Analytics benefit insurance? And how is it playing an ever-increasing role in the sector?

In insurance, the provider sells the insurance to the insured for a premium. Predictive Analytics is playing a growing role in insurance and is already being used throughout the value chain of , sales, underwriting, pricing and claims. It is equipping companies with the data to assess risk better, maximizing the return on their investments, improving customer service and increasing overall efficiencies.  

The benefits of Predictive Analytics outweigh the concerns about the use of data in a historically conservative sector. Most insurers want confidence in their Predictive Analytics. Today’s solutions, such as those created by startups in the Startupbootcamp InsurTech program, are supported by new predictive tools and database technologies combined with big data: offering superior, secure and solid behavioral information.

Widely accepted for customer acquisition, retention and cross-selling, Predictive Analytics helps target the right customers and predicts those who may leave. Acquiring new customers costs five times as much as retaining existing ones.

For example, Predictive Analytics is an important defense against insurance claims fraud. According to the Coalition Against Insurance Fraud, it’s one of America’s largest crimes, with at least $80 billion in fraudulent losses each year. These losses don’t just affect insurers, but policyholders as well since ultimately premiums must reflect all losses. Predictive Analytics can be used to better detect and “flag” potential fraudulent and duplicate claims.

From a finance perspective, by combining past losses with Predictive Analytics on future losses, the ability to manage loss reserves is also greatly enhanced. Funds can be more efficiently allocated, improving margins through improved cash management.

But there are likely to be barriers and hurdles to overcome along the way. Integrating Predictive Analytics with business processes will not be straight forward. Spotting difficult claims early can often be harder; working out strategies to mitigate the risk once identified is trickier still. Bridging the gap between a data science team and a claims organization will take some work.

It takes more raw data, and more observations, to spot large claims than it does to settle small claims on an automated basis, simply because small claims make up the bulk of observations in any given data set. It requires even more data to build an artificial intelligence (AI) capable of handling the complexities of the largest claims. The inherent complexity of each large claim tends to mean they are more individual than the high volume, low value ones that can be fast tracked.

To solve this challenge, insurers are turning increasingly to external data sources; adding more information about a claimant or injured party, such as identity verification or social data. The purpose of this is to paint a more complete picture of a claimant, which may assist, speed up, and reduce costs in claims processing.

Predictive Analytics is the future, not only in insurance to improve cash flow but in all industries that predict risks. Zasti, one of the Startupbootcamp InsurTech London 2018 startups, lives and breathes Predictive Analytics, and uses it across multiple verticals including aviation, property and healthcare.

Ram Srinivasan, Chief Strategy Officer at Zasti, says:

“Insurance is about predicting risk and that’s what Predictive Analytics is about. It’s about bringing science to the process – no more ‘gut feel’ – it’s no longer based just on experience. It’s about data and how we use that data. For example, if I could predict when an aircraft is going to be delayed, imagine the kind of opportunities that presents an insurer. Starting with getting the claims processed faster, to actually identifying, by working with the airline, to stop the delays. That’s what Predictive Analytics is about.”

CyStellar – another in the London cohort – is a data analytics platform that uses data from multiple sources such as satellite imagery, drone surveillance weather forecasts and other social, political or economics sources, to help organizations make better data-driven decisions.

“We provide three levels of data analytics insights to insurers to help them make better decisions: Descriptive Analytics by using data to explain what has happened in the past; Predictive Analytics to understand the future and warn the insurers and their clients of damaging events that might result in future losses; and Predictive Analytics to advise insurers what they or their policyholders should actively do to avoid damages,”

said Peter Bunus, CEO and Co-Founder at CyStellar.

By using Predictive Analytics, insurers can be more cost effective in anticipating and avoiding policy triggering events that usual result in damages and costly claims.



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