Insurance solutions with AI

Three Applications of AI in Insurance

By Marta Marino and Jack Hampson
10 minutes read


In this blog, we will walk you through three applications of AI in Insurance, using our Skim Engine’s capabilities with unstructured web data, and the ROI from this data revolution for your organisation.

Insurance 4.0


This year is predicted to be a year of disruptive innovation for the insurance world commonly dominated by age-old technology. In recent years a large number of challenger startup and scaleup businesses have looked to disrupt the industry with data and Artificial Intelligence at their core. Those that have already adopted AI solutions have seen huge growth, but there is still so much more potential for disruption and innovation out there, especially by those thinking about a holistic data and AI strategy, that delivers value for both the insurer and the insured.


Data is now the driving force behind so many areas within insurance. But typically, this uses internal structured data, or occasionally external data such as weather and telematic for things like underwriting risk or claims processing. But, there’s another source of data that many don’t think to innovate with; web data.

Why unstructured web data – why Skim Engine™ ?

80% of the data available is found in unstructured form and it’s out of reach for businesses that are still using traditional technologies. This data comes from sources such as social media, internal collateral (i.e. presentations, marketing and sales collateral, reports) or in customer-generated content on the web (i.e. reviews, comments, emails to your teams) and even your customers’ public websites.


Unstructured web data can enhance your view of the world, an industry or a client. It will reinforce your business decisions by giving you a deeper understanding of customer behaviours and opportunities. With unstructured data, you will be able to carefully listen to your customers instead of guessing their feelings around buying decisions. You will be able to identify opportunities to innovate your products and to fill innovation gaps with new features your competitors are yet to produce.


The Skim Engine™  was designed to take advantage of all of this unstructured data. By using Machine Learning and Natural Language Processing to find context and understanding within the raw data. Turning unstructured into comprehensive, machine readable data for future AI products or big data analysis.

3 Applications of AI in Insurance

Since the increasing adoption of AI in Insurance and the use of valuable data, we identified three areas where AI and unstructured web data can be applied and bring value:

  1. Profiling and Segmenting Customers
  2. Churn prediction
  3. Lead Generation and Marketing Automation


1. Customer Profiling and Segmentation


Nowadays customers are producing more data than ever before, in return, they request tailored and on-demand solutions to their needs and wants. The challenge for you is to identify customer profiles that generate the highest revenues and those that cost you.


Besides the amount of time and effort that your teams have to invest in manual customer profiling, the biggest limitation is in the type and quality of data you have available or are capable of analysing, typically limited to CRM, Policy and Claims data. This challenge isn’t just constraining your ability to profile and segment your best customers but it’s also keeping you away from more relevant information you might need to know for a customer group that could lead to a missed sale, brand and reputation damage or churn.

The solution to your challenge:


Machine Learning and AI aren’t just the latest trends, they are changing the way businesses are interacting with their customers and they are increasing the customer’s expectations, with faster and more personalised services.


Businesses that utilise Machine Learning algorithms and more detailed fine grain data from the web (unstructured data) have more visibility of behaviours, tastes, and trends than before. The insights gained from these narrower, refined segments can be used to personalise content (and more), without damaging the brand or losing profitable customers. This key approach is known as the “segment-of-one”, in which AI algorithms provide tactics to engage with your customers on a personal level.


Using unsupervised Machine Learning to segment customers with enriched profile data from the web, one can find new previously unconsidered attributes by which to group. How is this useful? Consider insuring a business for public indemnity insurance, but that business doesn’t have a customer support team, or never replies to complaints on Twitter. This external information gleaned from the web can be used to segment that prospect into a higher risk category.


Or, perhaps there’s a customer group that’s more profitable than another. But all of the standard “name”, “address”, “industry” data you have is the same for both groups. How do you find more customers from the profitable group than the unprofitable group? You need to find the hidden attributes that link the profitable ones. Be it behaviour, profile or activity data; by adding more sources to your analysis, you’re more likely to find a connection and therefore more valuable clients.

2. Churn Prediction


The insurance industry has changed over the years; customers are exposed to information whenever they need and through their preferred channel. It has become a very competitive market with customers looking to match their needs with the best prices available by easily switching from one supplier to another, only made easier by the rise of Price Comparison sites. But, customer loyalty is at a breaking point for many other reasons than just price such as; their overall experience, the relevancy of the benefits provided, their personal circumstances and more.

It has become nearly impossible to understand and to predict when and why a customer leaves using traditional methods. To retain your customers you need to pay attention to all the red flags that customers give you, by analysing their behaviour, understanding the sequence of events that lead to churn and trying to put preventative measures in place. But so much data to analyse makes it impossible for a human to perform.

Rule based systems are only so good up to a point, but with dynamic changes in markets, tastes and needs of customers, only something as powerful as Machine Learning for Churn Prediction can consider all of th additional factors.

The solution to your challenge:


AI-powered churn prediction tools effectively help you anticipate the likelihood of a customer to stop any transaction with your business by analysing thousands of additional data points. These tools work by cleaning and processing the required data, building a predictive model, and identifying those customers prone to churn risk. Your teams can then decide the best action to take to retain customers and keep them engaged whether that’s by automated or personal engagement.  


Furthermore, to improve your churn prediction models (whether they’re Machine Learning models or rule based one), you can use your newly enriched customer profile models, that use unstructured web data, to group churn and non-churn customers. By adding this deeper understanding of a customer, you’ll find more insights into reasons for churn and can take action with higher performing re-engagement tactics that are personalised and effective.


The implementation of these tools will lead to an intelligent, informed and smarter customer experience and journey, in a churn reduction and greater Lifetime Value (LTV).

3. Lead Generation and Marketing Automation


Whether working with inbound or outbound sales and marketing teams, the job of identifying and qualifying a sales lead can be tedious. Sales Development Reps (SDR’s) trawl the web looking for opportunities that are passed on to telesales teams to bombard with calls or mailshots. This approach wastes time and money on targeting unqualified, cold leads.

What if you could tie in the profile attributes of a good customer (high ARR and LTV), to build search criteria for new leads? That way you know the sales teams efforts aren’t wasted on low margin business that is highly likely to churn.

The solution to your challenge:


Using the previously developed models for customer profiling and churn prediction, you can build a web crawler that looks specifically for attributes associated with good customers. From there its a case of identifying the contact information of a business, using the Skim Engine to extract address and telephone contact details and automatically generating a lead list of qualified leads.


Once in place, this system will alert your teams with sales-ready leads to engage. A further AI system can identify a lead’s propensity to purchase based on the prospects activities that mirror those of already won clients, this way your sales teams can focus on nurturing and converting prospect customers and less time on cold calling.


Here is another possible application of AI which consists in the creation of a Marketing Automation tools that can find, sort and send the right information to your prospect at the right time to nurture leads effectively. By understanding more about your customers, based on social media, and public web data, you can hone content strategies to encourage a lead down your sales funnel. They need to know about you, your products, what is best for them and all that is relevant for them to make that fundamental decision, but this can be done proactively based on the buying signals they show.

Your Return on Investment


Time is costly to your teams and how they are using it is costly to your business. You want your teams to put their energy in the right leads that can generate revenue and at the same time, you want your customers to have a great experience when using your insurance products.


AI-powered Machine Learning systems are proven to maximise 30% of your team’s time and in some cases has increased revenues by over 50%.


The ROI goal you should set from this implementation is to find the right customers (focus on customer revenue-generating), at the right time (catch the perfect moment without missing opportunities), with the right message (relevancy and personalisation is key).

Data Science Consultants at Skim Technologies:

As Artificial Intelligence develops at such breakneck speeds with new technologies coming to market on a weekly basis it can be hard to keep up. Skim Technologies can help navigate the complexities and empower Insurers and Brokers to become data-driven and innovative with AI in their approach to Customer Profiling, Churn Prediction and Sales & Marketing automation.

Our Data Science Consultancy Services will help you:

  • Understand what can be done,
  • Identify the potential by making sense of data and or building new products,
  • Innovate by delivering tailored solutions and by thinking outside the box when it comes to tackling your project and finding the best way to empower you with data.


About Skim Technologies Skim Technologies is a Machine Learning and Data Science Consultancy that builds Artificial Intelligence (AI) solutions for scale-ups and enterprise clients. Skims powerful NLP Skim Engine™ extracts and structures web data to accelerate the development of AI, insights and automation for businesses looking to innovate with external data sources such as News, Competitor Data, Market Insights or Alternative Data sets. The company has offices in London and Portugal and works with clients globally.

Our mission

Skim’s mission is to empower people to use data more effectively and to demystify artificial intelligence. Rather than holding up the common narrative of machines replacing humans, we see how machines can help humans to have easier lives and better businesses.

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