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We recently spotted an article in Insurance Times in which Hubb Insure’s COO Ed Halsey predicted that underwriters will be ‘dead in the water’ in just 10 years as a result of data becoming more prominent in the insurance market. He claimed that relationships were too prevalent in the sector and that “all things are better done through data”.
With data becoming more essential in the insurance industry Halsey suggested that underwriting could soon become a thing of the past.
As the provider of Customer Lifecycle Intelligence (CLI) , powered by data analytics, artificial intelligence (AI) and machine learning (ML), you might think we’d agree. That we have some masterplan to automate underwriting to the extent that humans are no longer required. But you’d be wrong.
We align ourselves more with another commentator from this story. Phil Williams COO at Clear Group felt that underwriters would continue to exist despite the very real strides organisations are making in the use of AI and ML. He predicted instead that humans would continue to play an integral role in the underwriting process.
We too believe underwriters are better with data. So, let’s explore how data and insights are augmenting human underwriting rather than replacing it.
The digitised underwriting advantage
Underwriters with the most advanced data and analytics capabilities enjoy better operating results and performance, according to a recent report from McKinsey. The global management consulting firm claims that underwriters can see loss ratios improve three to five points, new business premiums increase 10 to 15 percent, and retention in profitable segments jump 5 to 10 percent.
Instead of being dead in the water, McKinsey believes underwriters of the future will be “portfolio managers”—empowered by artificial intelligence (AI) and digital - operating like hedge fund managers with increased leverage, scale, and insight. They believe underwriters will:
- Be able handle substantially larger books of business with more precision and control
- Use data throughout the underwriting process to inform underwriter decisions in prioritisation of prospects, validation of exposures, policy structuring, and pricing
- Rely on continuously evolving risk models that incorporate ever-expanding views of risk characteristics, tailored by line, segment, and emerging loss trends
Let’s explore how.
Increased precision and control
The process of receiving an inquiry, assessing the risk, and delivering a quote back relies on a wide array of customer and market intelligence. Data and technology can optimise the process enabling underwriters to work with increased speed, precision, and control.
Rapidly unifying data from billions of sources (both structed and unstructured) to optimise workflows, reduce risk, attract capacity, and reduce speed to quote. Whilst also allowing underwriters more time to focus on improving the customer and broker journey, winning, and retaining more customers, brokers, and markets, and operating larger books of business.
Rapid triaging of submissions
A hard market means an increase in propositions received from the broker market, but with reduced capacity. Thanks to advances in data science underwriters can, at the click of a button, screen companies against key financial metrics, flag potential risks & gaps in information and be alerted to potential moral hazards, including PEPs and Sanctions, that could prevent them placing the risk on cover.
Better decision making and precision pricing
The technical task of assessing each risk and making decisions about terms, ratings, exclusions, and pricing of a risk has always been part of the dark art of underwriting. Carefully leveraging data and technology augments this fundamental skill, allowing underwriters to making better, more intuitive decisions, faster.
Surfacing millions of structured and unstructured data points and leveraging AI and advanced analytics underwriters can build a complete 360° view of a customer risk profile and can therefore get to risk selection faster with improved decision-making, more accurate pricing, and improved loss ratios.
Proactive Risk Modelling
In a dynamic risk landscape underwriters are increasingly required to explore ways to make risk acceptable for insurance coverage, as well as keep track of emerging risk profiles. And they need to this at scale. But, in reality, without knowing the full story of a business its market and other external factors, underwriting at scale is difficult to get right – and pricing accurately and competitively is even harder.
Using a rules engine allows underwriters to automatically identify if a prospect fits its risk appetite. AI and ML can also be leveraged to learn from historical cases and classify new risk categories based on analysis of rich, contextualised data.
Continuous risk mitigation
With advanced data analytics and workflow automation customer lifecycle risk monitoring can be done proactively, not reactively. Likewise, MTAs can be addressed far more easily, and underinsurance avoided by using rules to automatically flag the changes underwriters need to know about as they occur.
Risk categories can also be reclassified and changes to the impact of specific risks can be factored into existing underwriting models to proactively mitigate risk and advise customers of corrective actions to be taken.
So, do we think underwriting will be dead in 10 years? No, we don’t.
Yes, the role of the underwriter will change. The future of underwriting will take a blended approach of human and digital.
Judgement and skill combined with advanced Customer Lifecycle Intelligence.
To find out how you can improve underwriting efficiency, enrich submissions, improve decision making, assess and monitor risk, price more accurately, improve the customer experience, reduce costs, and gain a significant competitive advantage get in touch.
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