Deploying Machine Learning For Insurance

Deploying Machine Learning For Insurance

August 1, 2017

4 Min Read


This week, AI Business’ guest writer is Ian Foley, founder, and CEO of acuteIQ, which uses predictive analytics to help finance companies find new customers. One of the core underpinnings of AI effectiveness is data and Foley gives us his insight into how AI is transforming insurance and how companies can best prepare for change.

Data is poised to be the next currency and the sooner insurance companies can start to leverage their data, the quicker they can take advantage of other revenue driving tools that also depend upon normalized data.

In a recent PwC survey, 74% of insurance companies believe that some part of their business is going to be impacted by technology disruption and 1/3 of traditional operations might be lost to FinTech. To address this, many companies have looked to new technologies like Blockchain and customer Bots to adapt. However, better managing and extracting value from an insurance company’s data are easier places to make significant changes and, most importantly, are more effective in creating the building blocks for sustainable competitive advantage.

Historically, insurance companies see data through the prism of better analytics to make more informed underwriting or actuarial decision-making. For example, 70% of insurers believe that FinTech’s primary impact around data will be to generate deep risk insights. The same thinking also occurred in the lending industry over the last four years, but this story did not play out so well … non-traditional lending risk assessments among alternative lenders has been the reason a number of these firms (e.g. CAN Capital) have got themselves into trouble.

One area that has significant potential is to use data to help with the customer acquisition process. Traditionally, insurance agents have relied on relationship selling supported by lead generation tools (e.g. Dunn & Bradstreet). Now, new tools exist to help insurance carriers start to predict customer needs for insurance products.

These tools use predictive analytics to look for ‘active signals’ of customer intent and then tie in relevant insurance products. For example, knowing that a construction company has just won a large contract is a good signal that they might want additional umbrella insurance. Also, knowing that a business has just secured its first institutional round is a good signal that the firm needs Directors & Officers insurance. Broadly speaking, these predictive signals can be categorized into those that use algorithms to look for specific events (e.g. winning a contract) or business life cycle activities (e.g. starting a business).

Moreover, extrapolating data to predict trends can be used for ‘look-alike’ modelling to identify other related businesses that have similar characteristics (e.g. revenue size, industry type, location) to an insurance company’s existing customer base.

Normalizing Data

A lot of this data already resides inside an insurance company’s business, but it is buried in siloed systems that do not talk to each other. This can be tackled by a combination of normalizing the data to create a structured data set and/or training algorithms from a third party data sets. Once the data is normalized, insurance companies can start training their models and using their own field sales teams to refine the algorithms. The primary benefits of this approach are that it can generate immediate revenue and cost savings.

Companies that have implemented these types of changes rarely publicize the benefits. However, at acuteIQ we have now worked with some of the leading insurance firms so can share general observations. We utilize our database of 21 million small businesses, which we apply machine learning algorithms to identify purchase intent signals, and have seen a doubling in higher quality leads into the conversion funnel and 80% time reduction to originate new qualified prospects. By finding better-qualified prospects in the funnel, the new customer underwriting process tends to be near twice as fast.


Data is poised to be the next currency, and the sooner insurance companies can start to leverage their data, the quicker they can take advantage of other revenue driving tools that also depend upon normalized data. Furthermore, with many other potential projects vying for the attention of budgets, internal champions have a better chance at making a change to an insurance company by leading a project to optimize internal data rather than starting with the more eye-catching Blockchain or bot projects.

acuteIQ is a customer aacuteIQ.pngcquisition platform that uses AI and 1st party data to deliver a 4x improvement in performance. Using acuteIQ's technology, marketers can increase consumer engagement throughout each step of the consumer buying experience, from brand awareness to conversion.

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