This latest Opinion piece is by Amrit Santhirasenan, CEO and cofounder, hx and looks at how tech and multiple data sources can help to price insurance risks more accurately.
Almost every person in the world is walking around with a smartphone in their pocket, some also have smart watches, health trackers, smart cars, and intelligent keys. Consumers are becoming tech savvy and many businesses are catching up. So why does the insurance industry lag so far behind? Hampered by legacy tech and processes, many insurers are pricing 21st-century risk with 20th-century tools. But it’s all changing.
Consumer needs often drive business decisions, and just as consumers started to buy and use the latest technologies, businesses started transforming digitally. Some industries consider that term old hat now, but insurers still have a long way to go. A thousand rows of data modelled in Excel may have been considered standard in the past, but today it should be seen as inefficient, unreliable and totally inadequate. Yet there is still an unrelenting reliance on tech built in and for 1997. It is simply not fit for purpose in today’s world where 2.5 quintillion bytes of data is being created on individuals and businesses every single day.
Risky business
The staggering rate of technology innovation and usage is creating a new dimension of risk never previously seen. Ten years ago, for example, fully autonomous vehicles were the stuff of sci-fi shows and a certain billionaire’s wishlist. Today, regulations are being introduced to legalise and insure such vehicles – although not quite on the roads yet. But how do you price risk for unknown liabilities or assets like self-driving taxis?
Assessing risk is all about taking the known factors and using these to price the unknown. For example, which past data can be legitimately used to predict the risk of new technologies never before seen? Which data should be avoided? And how quickly can you react when claims start coming in revealing the model you’ve used wasn’t quite right?
Underwriters need access to the right tools and infrastructure for a more forward-looking risk assessment and real-time portfolio management, enabling effective assessment of accumulations and marginal risk impact. This approach of failing fast and reacting is vital to meet customer needs and stay ahead of competition. But spreadsheets do not react, they are updated – manually and slowly.
The rise of price modelling tools
This ageing technology is often forced upon underwriters and actuaries, which is only really good at decreasing productivity levels and job satisfaction. Many large insurers started to realise the potential of technology to unlock value in growing pools of data. They needed intelligent price modelling but they didn’t have the know-how to build the tools to deliver it. Millions of pounds and several years later, many failed – some are still trying. But building tech generally requires a tech mindset, which is incongruous to the insurer mentality of wanting to be insurers and not tech companies.
Filling this void, insurtech companies emerged creating products like Renew, giving actuaries and underwriters the ability to build and use simple or complex pricing models that turn unstructured and structured data into actionable insights.
Previously, building out a global suite of pricing models with a connected database was costly and time-consuming, often requiring months of specification time and development work. Now, some price modelling tools can enable actuaries to build models ten times faster, in days rather than months.
Reducing the skills gap
High demand and short supply of pricing actuaries and underwriters combined with disruptions driven by digital adoption make the talent crisis even more pronounced for insurers. Investing in next-generation pricing tools, processes, and strategies is essential to optimise the underwriting workflow and create better, more efficient working conditions for employees and prospects. This helps retain talent – but you still need to attract the right people in the first place, and new technologies are changing what those people look like.
The most effective pricing tools help organisations be more data-driven and enable better risk selection. But there is a skills gap when using these new tools. Data doesn’t come in the form of a chart with easily identifiable insights, and coding knowledge essential in disseminating these insights isn’t something often taught to underwriters and actuaries. Understanding the coding languages used for Artificial Intelligence and Machine Learning like Python and R is increasingly important for those pricing risk, and it’s driving an increase in demand for workers with technology skills. The challenge here is making a historically traditional industry attractive for data scientists who are being lured by “new tech”. Proving a focus on tech enabled innovation is a good first step.
There’s no doubt that technology adoption is essential to establish the future of insurance. In terms of modelling risk for tomorrow, underwriters and actuaries need pricing tools that can evolve quickly and in direct parallel with the marketplace. They should be the building blocks upon which price models are built, and they should require as little learning as possible to use.
As the insurance market evolves, businesses with the best pricing strategies and infrastructure will be best positioned to win.
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