MOVING INSURANCE TO A PREDICT AND PREVENT MODEL

By Manan Sagar, CTO for Insurance, Fujitsu UK

 

In Philip K. Dick’s The Minority Report, three future-gazing mutants are used to alert the police force about crimes before they are committed. The premise is, by predicting when and where crimes will happen, they can prevent them from happening at all.

While Dick’s novel is fantasy, the idea of preventing incidents from happening can be transferred to the real world. And the insurance industry has begun to use data to move from the classic ‘repair and replace’ model to ‘predict and prevent’.

In essence, this means a shift from reactive insurance policies to proactive. In being reactive, customers purchase insurance policies and nearly immediately forget about it… until an accident happens.

But by using data to predict potential incidents and damage, insurance companies could reduce the likelihood and severity of losses suffered by their customers and consequently improve their operating ratios.

 

Manan Sagar

Too little, too late

Currently, many traditional insurers use a system where the customer pays for a policy, which is then left or forgotten about until something happens; they then make a claim.

Take home insurance policies, for example. Generally, consumers will purchase and leave them. While everything is fine, there’s no issue; however, it becomes a very expensive problem for both the insurer and customer if something does break.

But with the massive amount of data points insurers own, they can change the way policies are not just bought, but also utilised by customers.

Water damage is the number one cause for home insurance claims, and if the customer gives the insurer access to those data points, they could help to prevent it. The insurer is alerted when there is excessive water pressure in a particular household, which would lead to pipes bursting. This activates an insurance claim, which interacts with the water company to fix the issue and the potential incident and consequent losses are avoided before the loss happens.

This is possible to do at scale with the automation technology now available and the prevention focused model could work for many different areas of insurance. It will build trust between insurers and their customers by demonstrating value and insurance premiums will begin to be viewed lesser as an “annual tax” and more as a “service charge”.

 

If it ain’t broke, it might still need fixing

Some may argue that the current model of insurance is working. Companies are, for the most part, remaining profitable and change could be viewed as risky by key stakeholders.

But the predict and prevent model can help insurers save in instances that have been a thorn in the side for many years.

Take fraud – it’s a massive cost to insurance companies; it was estimated by KPMG that in 2018, fraud cost UK insurers £1.2 billion.

While predict and prevent won’t completely eliminate fraud, the number of cases would drop. If someone is claiming they had a car accident, there is enough data out there to get the full picture of what happened.

Traditional insurance companies are also under pressure to buy into these services because of the cheaper alternatives that are hitting the market. As with other areas of financial services, insurance is being disrupted by a number of insuretech companies that are agile and cheaper than traditional insurance.

With all the money-saving comparison sites, consumers are savvier than ever about what company will get them the best deal. Recent research found that households could be losing up to £1,400 a year by not changing car and home insurance companies each year – meaning long-standing customers are being incentivised to change insurers.

This comes back to an important point around trust. If customers cannot trust their insurers to give them a fair price, why should they continue to pay for their services?

When there are cheaper alternatives available, traditional insurance companies need to demonstrate value by helping their customers reduces their losses by focusing on prediction and prevention.

 

Unlocking the power of data to foster a new relationship

The “predict and prevent” model works by analysing vast volumes of data to find patterns in the causes of particular risks. By identifying the beginnings of these patterns in real time data, interventions can be made before the pattern plays out. Think of a manufacturing business. By tracking the health of factory machines right down to individual components, insurers can not only create more accurate predictions about their likely operational lives, but actually recommend timely repairs to stop the machine from breaking down, saving not just money on both sides, but also reducing accidents at workplace. The power of data can be similarly applied to aircraft or locomotive and in maintaining railway, water and energy lines.

The focus on preventative rather than reactive activities can help to change the role of the industry as a whole. Organisations can focus on getting things right for the future, rather than addressing the mistakes of the past. That will help to create a culture based around social purpose – that is of growing importance to both employees and customers today.

 

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