For 50-odd years, the way insurance has worked has remained the same. But in the last few years, catalysed by the pandemic, the rise of digital solutions and insurtech looks to break down historical insurance preconceptions have emerged.
Two of the biggest game changers in the insurtech industry have been the introduction of artificial intelligence (AI) and machine learning (ML). Together, these two technologies have uprooted the traditions of the insurance sector and reworked it completely. Understanding how they have done so, we reached out to the industry:
A move to continuous underwriting
For Leetal Gruper, co-founder and CEO at Tarci, a continuous intelligence engine that generates dynamic SMB data, there have been two big ways in which AI and ML have changed the industry.
“Firstly the impact of continuous underwriting. With the advance of data driven by AI, forward-thinking carriers are moving from one-off underwriting (once a year at the point of binding a policy) to continuous underwriting that changes and adjusts as client needs change
“Secondly, we can see their impact in renewals. Carriers now no longer just look at industry-level statistics when it comes to pricing a renewal policy. With advanced AI, carriers can now tap into any risk changes that happened at company level data (even in SMBs) that allow them to renew policies in the most efficient way.”
Tailoring policies to the policyholder
The importance of personalised products was highlighted by Kartik Chopra, CEO at Devron. Devron is a data science and ML platform company dedicated to unlocking the innovation and insight of data without compromising privacy. He said: “The increasing use of machine learning in insurance-based underwriting is allowing businesses to rapidly trawl through voluminous data points related to the prospective customers and policyholders.
“Machine learning models analyse available data about such customers and their behaviours to identify key risk factors. Once these factors have been identified, AI underwriting applications can formulate insurance policies with guidelines and costs that are aligned to the individual policyholder to deliver a more personalised product and risk management for the insurer.
“Those who are most effective in these efforts are leveraging rapidly evolving data-centric technologies such as federated learning, which allows them to incorporate varied, distributed, and even non-traditional data sources into their models without the risks and overhead of data movement and while protecting customer data privacy.
“More advanced firms are also delivering ongoing services and guidance personalised to policyholder behaviours to further manage risk. Those firms that are able to access and manage diverse data sources effectively are able to differentiate their products and services while better managing risk.”
Making the previously impossible, possible
John Beal is the senior vice president, analytics, insurance, at LexisNexis Risk Solutions, a global data and analytics company. Beal provided examples of how AI and ML have been implemented in the household and motor insurance sectors.
“There is no shortage of data in insurance. AI and ML are helping insurance providers to operationalise all of this data. Bringing it in at the right time to support quotes, price a risk, expedite a claim, flag possible fraud or understand a cross-sell opportunity. It is also helping consumers and businesses mitigate their own risks, to help prevent a claim from occurring. Imagery AI is also fast becoming a valuable tool across multiple insurance lines too, from tracking macro climate changes to swiftly triaging claims damage.
Household insurance
“In household insurance, prefill and data validation solutions help make the whole application process quick and simple, no more guessing at rebuild costs or property age. This is only possible through a huge amount of modelling, linking and AI-ML techniques to pull all the data together to return accurate and up-to-date information on the person and property. AI can also provide valuable insights around the footfall, crime rate, exposure to perils or other local circumstances that increase risk to commercial properties.
Motor insurance
“In motor, an ADAS classification system using machine learning to scan millions of lines of car manufacturer vehicle data means insurance providers can now understand the presence and performance of ADAS on a specific vehicle. This opens an opportunity for consumers to receive valuable discounts for the safety features on their specific vehicle. Motor insurance claims are also benefiting from AI/ML techniques where image recognition technology captures damage or invoices. A system audit automatically authorises and settle the claim if the criteria are met.
“Finally, pricing insurance is based on predicting the risk of a claim. Here machine learning algorithms can speed the identification of the most predictive attributes behind claims losses for use at point of quote.”
The pillars of insurance’s future
David Fontain, CEO of Foresight, the workers’ compensation insurtech, was extremely happy with the adoption of these technologies. He compared the processing time for data with legacy systems to the processing time with the new technology. He believed there was a clear advantage in favour of using tech.
“AI and machine learning are pillars of the future of insurance, across all lines of business and sectors. From cybersecurity, to blockchain, technology is influencing all aspects of insurance and challenging the traditional business models the industry has long held as standard.
“Insurtech has a little more agility and pace of play in developing and implementing technology than legacy carriers because we aren’t bogged down by decades of policy data, some of which was initially written on literal paper, which takes years to transform. So for brokers, tech-enabled insurers provide a competitive advantage in the marketplace.
“In our arena—the workers’ compensation space—Foresight using AI and machine learning to take the guessing game out of safety. Data produced by our proprietary safety tech and consulting services, Safesite, can pinpoint exactly which areas clients and prospects are at a heightened risk for worker injury or claims.
“We’ve recently completed a study that proves our ability to reduce claims frequency by 18 per cent for our policyholders in as little as one policy year. That’s the power of using our tech to turn data-driven insights into action items that literally save lives.”
Optimising customer experience
Tamara Zaichkowsky, chief digital officer at insurtech, Acrisure explained that the use of AI and ML didn’t need to stop at pricing models. She explained how other sectors of the insurance industry can use the technology to optimise the customer experience.
“When AI was introduced to the insurance industry, it was deployed for automation, specifically simplifying mass data analysis for pricing and plan selection. Recently, both Acrisure and the insurance industry have started to deploy this technology outside of just simple pricing models and into other sectors of the business to optimise the consumer experience.
“For example, Acrisure is leveraging internal AI platforms to help partners better prospect, develop risk-placement strategies and, finally, provide customised client solutions that deepen relationships and drive organic growth.
“As insurance brokers, our goal is to continuously improve consumer solutions and enhance their buying experience. As a result, we expect to see the industry continue to expand the use of AI and ML to align products with consumer needs by building more adaptable technology that can learn and grow as the consumer journey evolves.
“Especially within the growing direct-to-consumer digital marketplace, we know retention is the north star for our success and AI and ML will be key drivers in improving and customising the user experience and building long-term consumer loyalty and trust.”
