Protection  

Will artificial intelligence ever replace human underwriting?

Will artificial intelligence ever replace human underwriting?
Credit: Tara Winstead from Pexels

From voice assistants to personalised recommendations on streaming services, machine learning is a branch of artificial intelligence that many of us encounter daily and makes our lives easier.

While the protection industry has taken steps to enhance efficiency, such as with pre-underwriting tools, could machine learning have a role in making the underwriting process quicker?

“Naturally, all insurers want their new business and underwriting processes to run efficiently, delivering outputs and decisions as quickly as possible,” says Luke Harrison, head of data analytics at technology provider iPipeline.

“Insurers will strive to develop the perfect harmony between platform, data and people. That is, a rules-based underwriting system which is able to make decisions in real-time using machine learning to define parameters and controls, and an expert team that examines more complex cases/disclosures, using the benefit of their experience to make decisions.”

The current picture

Most insurers have an underwriting philosophy, which is usually interpreted into a set of underwriting rules around which companies create a question set, says Chris Monaghan, senior marketing manager at UnderwriteMe.

“The underwriting process asks a number of health and lifestyle questions and rules drive decisions from this data.

“For around 75 per cent of cases, this leads to straight through acceptance and the ability to go on risk immediately.”

At Vitality, for example, the majority of underwriting is completed using a rules engine.

John Downes, director of underwriting and claims at VitalityLife, explains: “Underwriting rules engines are pieces of software that use decision trees to automate and replicate the human underwriting process.

“These rules engines have been in existence for around 20 years. They work by asking high-level application questions, the answers to which use a decision tree process to further clarify answers if necessary.

“For example, additional questions may be asked about the treatment received and the frequency of symptoms of a declared condition, the answers to which will be used to determine the acceptance terms.”

Meanwhile, applications with a more complex medical history or those for larger amounts of cover will require a human underwriter, Downes adds.

At Zurich, applications are likewise run through a rules engine that performs an initial assessment based on the responses given.

“It’s worth noting that underwriting experts have developed and shaped this automated process, which will continue to evolve,” says Nicky Bray, chief underwriter for Zurich’s UK Life Business.

“This approach means that for the majority of customers, we can offer an instant underwriting decision, meaning that they can secure cover quickly and easily.”

But in cases where customers share information with Zurich about medical concerns or histories, and where the insurer needs additional medical information, all cases are looked at individually.

“Machine learning certainly has potential to help refine our initial process, though for more complex cases, human judgement will always be needed. Especially for conditions like mental health, where the symptoms and circumstances for each customer will be very different,” says Bray.