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How Underwriting will Change with Machine Learning

By Mary Kotch, CIO, Validus Reinsurance And Marie-Christine Razaire, Underwriter, Validus Reinsurance

Mary Kotch, CIO, Validus Reinsurance

Digital technology is changing insurance. The traditional insurance business model has proved to be remarkably resilient, and many legacy processes we use today are 50 years old due to regulation, compliance, and the preceding view that technology supports, not improve business functions. The Insurance Industry is in the process of being overhauled in an unprecedented way. Around 400,000 people will retire from the industry in the next few years. There is a growing concern that legacy systems and legacy cultures will hinder the industry from recruiting millennials and the following generations allowing for further disruption from external sectors.

The next generation insurance buyer, broker, agent, and underwriter are expecting instant access and information in regards to an insurance product. Transparency around pricing, data, policy wording, and product features will be expected at all times, not just after a claim. This demand for change is already being vocalized by the next generation of underwriters at traditional carriers.

With the rise of younger talent in the insurance workforce, it has become evident: you can’t expect people who have grown up in the 21st-century revert to using processes from the 20th.

One example of this is the underwriting workflow. In the last 10 years, you still had companies using paper only. Each deal would be captured in binders and stored away in filing cabinets. Data capture was minimal and the amount of time to process a deal was exponentially higher. Insurance companies then began switching to paperless systems—either using external software programs or their own ad-hoc process. This allowed for improved turn-around time but given the lack of format consistency amongst carriers and brokers for data capture, it still left a lot of opportunities for improvement.

Marie-Christine Razaire, Underwriter, Validus Reinsurance

Now with the availability to capture and extract data in a streamlined and cost-effective manner, the potential to analyze data exists in a way that wasn’t possible even five years ago. With the ability to enhance data beyond what is provided by a broker, it is now feasible to not only improve pricing results but use machine learning to score and rank each deal that an underwriter gets across their desk. DataCubes is one insurance company that is taking the lead and incorporating machine learning into the underwriting process. Underwriters will no longer have to blindly choose deals to prioritize and underwrite first. Manual entry of data entry is removed, eliminating redundancy and human error. Using the carrier’s appetite, each deal will be color-coded to indicate the likelihood of quoting. This will improve the underwriter’s overall efficiency as well as their quote/bind ratio. The broker-carrier relationship will improve as carriers are able to quote not only more deals but faster as well.

It will only be a matter of a few years before this machine learning approach to underwriting is found across the industry. Companies who embrace this next wave will not only be able to increase profitability but simultaneously recruit younger generations.

Technology like DataCubes is changing how products and services are delivered, and increasingly it will change the nature of those products and services and even the business model itself.

We predict the Underwriters’ focus will shift away from internal processes and specific transactions, emphatically toward market-facing relationships and sales. So where does that leave us today? And what is the path forward? By embracing powerful new technologies underwriters will be able to focus more on being an underwriter and less on administrative tasks. They’ll look to technology to enhance risk management and seek new ways to generate a premium. Tomorrow’s effective underwriters will demonstrate leadership and strategic engagement in areas such as innovation, product development, customer experience, and insight generation.

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