Artificial Intelligence (AI) in insuranceTrends that matter Part 1 of 3

Artificial Intelligence is one of the emerging technologies that is expected to disrupt the entire insurance industry in a very big way. Because the insurance industry is one that is dominated by a few major players with products that have not changed substantially over the years, most venture capitalists consider it as an industry that is perfect for disruption. A 2015 KPMG report predicts that the arrival of driverless technology will bring in much safer and will shrink the auto insurance industry by a massive 60% over the next 25 years.

There are three key trends that insurers should know about Artificial Intelligence (AI). In the first of three blogs on this topic, we will look at: Behavioural Policy Pricing — where the use of IoT sensors provide personalised data to insurers allowing for a more accurate and personalised risk assessment.

Current AI application trends in insurance:

Behavioural premium pricing: IoT sensors shift from an alternate source of data to the primary source of data

Data generated through IoT sensors provides rich opportunity for insurers to personalise insurance covers. Here are three ways by which they do so:

• Pay only to the extent of what you risk: Telematics and wearable sensor data provides for reduced premiums where less risky behaviour is detected this includes behaviours like driving less and exercising more.
• Bundle policy and loss mitigation: Smart home companies will offer policy discounts to users of loss prevention technology like secure digital access, smoke and fire detectors and connected security cameras, thereby facilitating the cross-selling of devices and insurance covers.
• Claims verification & settlement: The analysis and interpretation of the data received from IoT devices will reduce the time spent by insurers in the verification and settlement of claims by traditional time consuming manual methods.

The disruption that IoT causes in the insurance sector is similar to the disruption being caused in the finance domain by data science. It moves data in operations from an alternate source to the prime source of data for processing and decision making.

In the past, financial models were primarily based on carrying out statistical sampling of previous outcomes in order to forecast future outcomes. Insurance carriers depended on the risk pools that were constructed using processes of statistical sampling. But in today’s age, data science has brought in sweeping changes and advantages to these processes. Data science provides projections based on actual events in real time by utilising large datasets rather than samples. The use of IoT sensors in insurance also allows the pricing of insurance covers more accurately by basing them on the risk data of the individual concerned rather than samples of data from large groups.

An example to demonstrate this, is available in the domain of Usage Based Insurance (UBI) or pay-per-mile insurance. Here, telematics sensors that are fitted to the asset (an automobile) monitor the usage of the vehicle in real time and provide the data to the insurer. This means that safe drivers — identified by an analysis of the data provided by the connected telematics device, will need to pay less for their policy covers. These policy holders no longer pay for the risk profile of an entire pool but only for the risk based on their own driving habits. The only change that this insurance entails for customers are the installation of a telematics sensor in the automobile and also, being conscious of the way the vehicle is being driven and used.

Ringing in the changes

• Wearables and GPA are likely to drive the change. Innovation and implementation of out-of-the-box ideas will continue to reinvent this space for a long time to come. For example, in workplace compensation, a disability claimant can be monitored through a wearable for compliance with rehabilitation protocols. This will ensure that medically advised rehabilitation is properly followed through and the claimant will return to work as per schedule.
• Surveys show consumers accept this change. Today’s consumers — especially the younger generation are willing to provide access to biometric data to gain the advantage of better priced products. Health insurance is one sector that can utilise the data from wearable to personalise individuals health plans.
• Uncertainty prevails around usage-based insurance. In 2017, a report from the National Association of Insurance Commissioners stated that UBI was an emerging area with still a lot of uncertainty around the selection and interpretation of driving data and how the data should be integrated into existing or new price structures to maintain profitability.
• Most customers seem to love UBI. Surveys have found that people who have participated in UBI programs have made positive recommendations about it. This has often resulted in more of their contacts buying from their insurer than with those customers who did not participate in a UBI program.
• A segment of customers shun UBI. Some customers who were surveyed — twenty one percent, did not want to take part in the UBI program when it was offered to them and said that they did not want their driving monitored, did not think that they would save money or did not think that they would see a decrease in their insurance premiums.
• Analytics of the data is the key. Just the collection of reliable and relevant data is never enough. Along with great data collection, the proficiency of the analytics becomes vital. That’s where Application Platform Interfaces (APIs) play a critical role. Analytics are needed to derive driver behaviour scores, crash and claim analysis and a whole range of specialised risk analytics for fleet managers and car rental companies.
• Legacy players are slow to change. The 2017 Excellence in Risk Management report found that many insurers had no awareness about the newly utilised and emerging technologies like telematics, sensors, Artificial Intelligence (AI) the Internet of Things (IoT), smart buildings or robotics or their associated risks.
• Rapid change is not far off. Industry pundits in the auto industry expect significant turmoil in the sector once the insurance business models are overturned due to the impending massive market disruption.

Along with any new technologies comes the possibility of new risks. While sensor data decreases many risks and are beneficial for insurers in many ways, they can also be vulnerable to hacking. And, as sensor based data is vulnerable to hacking, they can face penalties under data breach laws. Considering these emerging risks, insurers may be able to develop and underwrite new insurance covers — as is already being seen in the bull market for cyber insurance.

With specialised solutions for the insurance industry, Neutrinos is helping leading insurers take impactful digital transformation decisions. We would love to set up a discussion with you to hear your specific requirements. Talk to our experts today!

References: https://emerj.com/ai-sector-overviews/artificial-intelligence-in-insurance-trends/