20 Sep

What We’re Seeing: Three Innovative Uses of AI In The Insurance Industry

Mo Beldo

The insurance industry is reaching an inflection point, with incumbents struggling in the face of growing competition from new, cloud-native insurance firms. The challengers are able to rapidly adopt artificial intelligence (AI) and start reaping the benefits. 

At Mesh-AI, we’ve helped insurers accelerate their data science programs and implement AI to solve their biggest challenges. And I’m seeing how these organisations are adopting new technologies to simplify business operations and make their workers’ lives more simple. 

In this blog, I would like to share with you three innovative use cases for AI that I’ve seen in the insurance industry around near-instant document processing, enhanced risk assessments and solving data quality issues. Let’s jump in.

Reducing risk and increasing sale prospects with near-instant document processing

The insurance industry is very document-heavy, many of which are still handwritten or scanned pdfs of handwritten documents, including lengthy policy documents and client claim notes. 

Parsing these documents manually for the key information is an incredibly time-consuming manual task. As a result, it is often outsourced to third parties, which carries a high cost, potential security risks and the output can be inaccurate. 

By using a combination of traditional ML and generative AI, insurance organisations can process their documents in a fraction of the time and reduce their reliance on third parties in the following ways:  

  • Converting handwritten notes into text 
  • Extracting key information (dates, names, locations etc.)
  • Summarising long documents 
  • Automating data entry 

Who is this useful for?

Underwriters and claims handlers will have their work lives turned upside down by this approach. Rather than trawling through pages of handwritten notes, they can use traditional ML models to scan documents, deploy generative AI to summarise them and then simply query them to instantly get the information they need.

How are insurers benefitting?

  • Massive time and resource savings: eliminate the vast majority of manual work from document processing and move people onto more value-add strategic work. This increases operational efficiency by reducing non-differentiated heavy lifting and maximising strategic work. 

  • Reduce dependency on third parties and associated risks: eliminate the need for third party document processing or extraction support. 

  • Improve data quality and availability: data from key documents will be more accurate, more usable and available more quickly. By processing claims faster, insurers can accelerate the time it takes to process a claim by extracting the key information from a claim report quickly and accurately.

  • Improve conversion rate: with data available faster you can accelerate the quote-to-buy process, improving the likelihood of a sale and increasing revenues. 

  • Risk-informed business decision-making: enhanced data quality means that risk exposure can be reduced when making business decisions.

Enhanced customer understanding and risk assessment

Insurance companies use simple models to determine the lifetime value and risk exposure of any prospective client and price their policies more accurately. Demographic data such as gender, ethnicity, age, historical claims, credit score and more are combined with scientific data to assess the risks for each of these profiles.

For example, how might new scientific data on the risks of long Covid influence insurance premiums? Or if a client moves to a more polluted city or a country with worse healthcare?

Keeping on top of all the ever-changing demographic and scientific data is a huge manual task, which is often outsourced to third parties. However, AI can carry out this task almost instantly. AI modelled on existing pricing can then ingest new data and studies, extract the key information and suggest how this might impact policy and pricing moving forward. 

Who is this useful for?

Actuaries suffer most from the substantial manual work involved in understanding their customer and pricing models. And the more out-of-date their models are, the greater the risk exposure. 

How are insurers benefitting?

  • Improve speed and accuracy of data extraction: what would take a human hours or days can be done near-instantly by well-trained ML/AI models. By limiting the manual work to final checks, human error is removed from the extraction process so it’s more accurate and people can spend time on more strategic activities. 

  • Improve and personalise pricing models: with better faster, more accurate data, pricing models can be rapidly improved and updated in line with the latest demographic and scientific knowledge, becoming more appropriate and personalised for each client.

  • Increase customer retention: by being able to offer personalised, responsive pricing based on each individual, you can offer a better customer experience that will increase retention.

  • Reduce exposure to risk: by continuously updating your models in line with the latest data (as with the long Covid example above), you reduce your exposure to risk.

Solve data quality limitations

Data quality is foundational for modern business success. Yet, many insurance companies lack truly solid foundations for their data governance.

Large Language Models (LLMs) and AI can be used to support data acquisition and integrity checks, as well as to infer data quality standards from data sets. Although this is still an evolving area for AI, it has the potential to be an incredible source of business value. 

Firstly, AI can be used to spot data quality errors, make suggestions for how to improve or even just fix them on the spot. Models can ingest a data set and pick out missing values and other inconsistencies that humans might miss or take hours to spot. It can then make suggestions on how to fix these. 

Secondly, it can infer missing data to fill the gaps. AI models can use freely available public data, third party sources or historical claims data from the relevant client or similar client profiles to create fresh data points to complete datasets. (These would then need to be validated manually).

Thirdly, you can use AI to generate data quality rules from an existing dataset to improve data standards. If you have a dataset that you know is high-quality, you can use AI to extract the rules that set is based on and overlay them onto other sets in order to bring them up to that same standard. 

These might not be 100% accurate right out-of-the-box, but they can make an excellent starting point and develop further to put insurers ahead of their competitors. 

Who is this useful for?

These use cases are helpful for actuaries and underwriters as well as the data teams that supply those roles with data. These roles stand to have much of their tedious, manual work eliminated so they can focus on their core competencies. 

How are insurers benefitting?

  • Improve data quality and mitigate risk: eliminate data inconsistencies/inaccuracies and elevate data standards across the board. This reduces the exposure that results from incomplete, inaccurate and stale data. 

  • Accelerate data processes and reduce overheads: by eliminating manual work AI can save huge amounts of time and make higher-quality data available in usable formats much more quickly. This also eliminates the costs of third-party processing and time-consuming manual work.

  • Improve business decision-making: make business decisions based on higher-quality, more up-to-date and highly available data.

How to Get Started With AI In Insurance

AI is going to completely change the landscape of the insurance industry and its impact is already permeating every corner of the sector. So how can you get started? 

Firstly, you need to get your data foundations in place. Your AI/ML models will only be as good as the data you feed them. If you want to know where you’re at in terms of data foundations, check out our free-to-use Data & AI Readiness Application

Secondly, you need an AI-ready data platform with the appropriate governance, standards and ethics frameworks in place. 

Thirdly, start by testing your generative AI models out on small use cases to learn and get some quick wins before you scale. This is where our Generative AI Accelerator can help. 

If you’re interested in hearing more, please reach out

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