ArticleAlgorithmic underwriting ESG Data Insurance Machine learning

A guide to algorithmic underwriting for commercial insurers

Anna Burge
Anna Burge04-May-2022

As one of only a few providers of algorithmic underwriting operating in the London market, we've written a comprehensive guide on the subject for commercial insurers.

A guide to algorithmic underwriting for commercial insurers

Algorithmic underwriting is one of the most sought after technologies by insurers in 2022. According to a Deloitte report from 2021, 25% of insurers have already expanded their automated underwriting practices, and there are no signs of the trend reversing.

This kind of technology is gaining traction because of the immense amount of benefits - especially in terms of the amount of time and money automation can save - that it brings to the industry. Insurers can improve the quality of the decisions they make, cut costs, and boost efficiency all with a single technology.

A recent report by Instech London highlights the fact that the global insurance industry is currently operating with an unsustainably high expense ratio. The widespread adoption of algorithmic underwriting and the benefits it brings is made inevitable by current market conditions.

Artificial is one of only a few providers of algorithmic underwriting operating in the London market. We've written extensively on algorithmic underwriting before because:

  • 25% of insurers have already invested in automated underwriting processes

  • Algorithmic underwriting is set to revolutionise insurance by 2030

  • Artificial is the only platform that can be endlessly configured to support data-rich, algorithmic underwriting

We believe we have the best technology in the market, and we can leverage any data source to algorithmically underwrite complex risks in real-time. But don't just take our word for it - let's explore the subject in more detail.

Understanding what algorithmic underwriting truly is

Much of the work of an underwriter relies on quantitative data, which means that algorithms can do at least some of the work. Insurers can set automated insurance underwriting software to work at multiple stages of the underwriting process:

  • Gathering data from customers and telematics including vehicle black boxes

  • Analysing data to calculate risk and learning new patterns and risk factors

  • Triaging applications on the basis of risk level

This significantly cuts down the amount of time underwriters spend sifting through relatively simple applications and claims. Automated triaging results in very low-risk applications being automatically accepted and very high-risk applications being automatically rejected while others are forwarded to expert human underwriters for further consideration.

You can read more about automated submission triaging or automated insurance underwriting on our blog.

Why use it?

Automated underwriting keeps insurers in control, allowing underwriters to set their own risk limits and price bands while speeding up and streamlining the process. This allows clients to receive decisions more quickly and gives underwriters more time to spend on other tasks.

By analysing patterns in policy and claim data, machine learning software tweaks its algorithms over time to reflect both inaccuracies in the original process and changes in customer behaviour. In other words, the sooner insurers embrace machine learning, the better their ML software will be in five years' time.

The benefits of algorithmic underwriting are many and varied:

  • Cost: 44% of respondents to the McKinsey Global AI survey reported cost-savings from AI adoption in the business units where it's deployed, with respondents from high performers more than four times more likely to say that AI adoption has decreased the cost of business units by at least 10% on average.

  • Time: Using AI software can help insurers to speed up the application process and free up underwriters' time to spend on more complex tasks, such as pricing, managing mid-risk applications, and increasing personalisation options for customers.

  • Customer experience: By automating the work of an underwriter even partially, insurers can make the claims process faster and simpler for both themselves and customers. In the words of the EY 2020 Global Insurance Outlook report, 'Better customer experiences have been a priority for years — and will remain one for the foreseeable future'.

  • Data: Algorithmic underwriting means that insurers can make use of a much larger data set than they can when underwriting manually, in large part because of AI's ability to extract meaningful information from big data.

  • ESG: Insurtech has, for a long time now, enabled insurers to make use of the large amounts of data they gather from customers, much of which is stored untouched. AI and ML software can help insurers to scan big data for patterns that track ESG data for clients in order to assess risk.

Does algorithmic underwriting work for your business case?

Insurers are recognising that all business cases of AI and ML technology will involve humans and algorithms working together. The Instech London report highlights the importance of human underwriters when working with automated solutions: 'The issue for anyone building algorithmic solutions is whether the machine is smart enough to know when it doesn’t know, and humble enough to admit it.'

For both brokers and syndicates, underwriting algorithms have the potential to change the playing field. For syndicates, Smart Follow technology allows insurers to automate the process of accepting syndicated policies which match specific criteria. For brokers, the automated triaging of applications saves time and money in a similar way.

Calculate your data needs

The number one priority of insurers keen to embrace automated underwriting technology should be storing, preparing, and tracking data. Instech London's report suggests that 90% of the work needed to implement algorithmic underwriting is in data preparation, not in building and tuning algorithmic models.

We've written in detail about the life cycle of data, which includes data extraction, data augmentation, and decision-making. Understanding the processes that data undergoes at every stage of underwriting enables insurers to see its impact on the final result. 

Want to know more?

At Artificial, we are experts in algorithmic underwriting. As one of London's leading providers of AI and machine learning tech for insurers, we believe that AI has the power to transform the global insurance industry.

If you want to know more about how you can implement algorithmic underwriting in your business, get in touch with us today.

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