It’s no secret the insurance industry is primed for disruption. All you need to do is type terms like “ai for insurance” or “latest insurtech” into Google search, and a veritable cornucopia of articles will pop up preaching the advantages of adopting new, intelligent technologies. With so much information available, it’s no wonder many insurers are struggling to figure out how to embark on their Ai journey in a way that ensures projects yield the desired results.
My study has reveal some common questions put forward by these insurers and this post is an attempt at anwsering them:
We need to be thinking about Ai, but we’re not sure where to start. What are some of the things we can do to kick-start the process?
Like most large-scale projects involving multiple stakeholders, developing a strategic roadmap for rolling out Ai is absolutely crucial. It start by identifying use cases and ensuring they are relevant by engaging the right business stakeholders and users in the process. Each use case should be carefully framed, with clearly specified objectives and desired outcomes. Once identified, the use cases can be assessed and prioritized based on a set of key criteria, including desirability, viability, feasibility and risk:
Desirability reflects the importance of the specific problem considered for end users (i.e. how badly do we want it solved?)
Viability evaluates the expected economic benefits or ROI (return on investment)
Feasibility assesses whether a solution to the problem is achievable, including data, technology and human intelligence
Risk gauges the potential adverse consequences of a model failure and the ability to mitigate against them
Its crucial to choosing a manageable scope, as developing an Ai roadmap for the company, one line of business, or a specific workflow will require wildly different efforts. Its advisable to commence with a more focused scope and expand across the organization in successive waves, each leveraging the learnings from prior iterations, rather than attempt to do it all at once.
What kind of data is needed to deploy an Ai product?
Data is critical to deploying any Ai technology. Data can be grouped into two buckets: structured and unstructured data.
Structured data is highly-organized and formatted in such a way that it’s easily searchable in relational databases. Structured data includes tables and spreadsheets –claims and payments that is stored in a company’s relational database.
Unstructured data has no pre-defined format or structure, making it much more difficult to collect, process and analyze. It can be in the format of text, images, audio or video and is typically much more of a challenge to search. Examples of unstructured data could include emails from brokers, financial statements, call center recordings, pictures of damaged vehicles and social media posts.
While most companies bave a mix of both structured and unstructured data, the majority of insurers’ data is unstructured. While structured data is preferable when deploying an Ai product, modern algorithms can leverage both types. In fact, recent advances allow Ai to effectively process many types of digital information, including scanned documents, images, videos and even audio recordings.
What can insurers do that lack data for deployment in Ai?
The first thing that needs to be determined is whether there is truly a lack of data or if what is really missing is labeled (structured) data.
Unlabeled (unstructured) data has not been tagged with one or more labels, and it’s these labels that enable Ai systems to accurately read the data and understand what it represents. Photos, audio recordings, videos and, x-rays can all be forms of unlabelled data. If it is an issue of data not being labeled, there are tools that can accelerate the labeling process and help insurers build Ai-ready datasets.
If the issue does indeed stem from a lack of sufficient data, then an insurer needs to rely more heavily on pretrained, off-the-shelf models. These products, however, tend to have lower predictive accuracy, since pretrained models do not account for the insurer’s unique specificities.
If we deploy an AI product within our business, will we be forced to migrate from our current digital platforms?
Migrations are messy, expensive, time-consuming and generally a painful process for most involved. This is one of the reasons why it’s crucial for Ai products to integrate seamlessly into your current workflows.
In order for algorithms to access data and provide a recommendation or automate a task, data needs to be available in digital format. This is certainly the case when a digital platform has been deployed. Even in the absence of digital platforms, some insurers have managed to apply Ai for certain use cases, but these cases are limited to areas where the data is available in a proper format. So in short, no migration necessary!
The insurance industry is highly regulated, and regulations vary by state. How does Ai take this into account?
Ai systems are not plug-and-play products. They learn continuously through mechanisms designed to capture human judgment, knowledge and experience, further improving the performance of the model over time.
Thanks to this continuous learning, industry regulations, including state-specific regulations, can be applied, for example by imposing constraints on the model structure or by limiting the data that is made available to the model.
Our company is currently deploying another new system. Is it possible to deploy an Ai model simultaneously?
In short, yes. In some cases it can be done. When deploying a model for the first time within your firm, the model will rely on historical data to be trained. This data would come from the older systems.
Insurers deploying multiple systems or platforms at the same time as an Ai model must make sure that the data coming from the new system is represented the same way as the data coming from the older system. Typically, newer systems provide more data with more granularity than older systems (which, when it comes to Ai, is preferable). It is therefore possible to deploy a model while deploying or migrating to a new system.
Our actuaries are already using advanced analytics and ML. Do we really need to look outside for other Ai solutions?
While some insurers’ in-house analytics teams have seen success developing Ai and ML models for specific uses, the reality is that it is very difficult to bring analytics to production. According to Gartner, by 2022, 85% of Ai projects will fail in production.
Given the level of complexity of advanced analytics and ML, compounded by the high probability of delays or failures of Ai projects, in the long run working with an Ai product provider can be a time - and cost - saving solution. Ai products can be deployed on-site quickly and they are highly customizable, thanks to human-in-the-loop features like tolerance thresholds settings for automation and corrective feedback provided by the end users.
What business areas are most primed for Ai?
There are opportunities to deploy Ai across every stage of the insurance lifecycle, with the technology currently available to us today, the areas where insurers will see the most return when applying Ai are in the underwriting and claims processes. However, there are also opportunities to apply Ai to other areas, such as marketing, customer targeting or product recommendation, to name a few.
At the end of the day, the best use cases to select are those that will best serve the organization’s priorities.
What happens when we are ready to deploy an Ai product?
Once the team has selected the use case, prepared the data and fine-tuned a model the next step is to integrate the Ai product into your tech environment, including all the systems it will need to interact with within the organization. This is done through API (application programming interface) calls. What’s an API, I hear you say ? It’s essentially a set of tools and procedures that help developers build software applications. The API will specify how the different software components will interact. An API call is the API being put to work. Any time you perform an action at your computer (e.g., send an email, download an app, enter a password), you are making an API call. In the context of an Ai product interacting with, say, your database, an example of an API call is requesting a price quote for a new business submission.
It can take anywhere from eight weeks to a few months to complete this deployment process. Unlike traditional RPA (robotic process automation) software, which is rules based and static, the model, once it is set up, will continuously learn and improve its decision-making over time based on thresholds you determine and control, as well as on corrective feedback provided by end users (e.g., administrators, underwriters, adjusters).
How should insurers plan for change management when adopting Ai tech?
Beyond developing an Ai strategy, securing technical talent, investing in data and technology infrastructure and putting in place the right governance framework and controls, insurers should not underestimate the importance of change management in ensuring the Ai solutions they adopt are successful (whether developed in-house or by third parties). As with any major initiative, insurers can plan for effective change management by identifying and empowering internal champions within the organization, in addition to developing and delivering a thoughtful communication strategy, both internally and externally, and providing adequate training and coaching, before and after deployment.
A practical list of activities and deliverables that insurers can start with to effectively plan for change management:
Preview your use cases to ensure that impacted business processes and SOP (standard operating procedures) are up-to-date. Its crucial to know what you are changing from, to understand what support, training and organization change will be required to be effective in the future
Update job descriptions and capture the tasks associated with impacted roles. This will help identify training requirements and determine whether one needs to reorganize job tasks or work assignments
Review the content and completeness of internal training and onboarding processes for the job roles affected. Once Ai is deployed, certain staff may be taking on new responsibilities and may require training or support to be set up for success
Communicate, communicate, communicate. It can’t be said enough! Companies should not only share the vision and roadmap for how Ai will be developed in the future but also how employees will be affected and the support systems that will be available to them to ensure a smooth transition for all
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