(Forbes) -- Artificial intelligence has opened doors for the life and annuity insurance industry with opportunities to enable customers and agents with a unique, personalized experience.
Whether it is used to deliver tailor-made products or to service customers on their terms and help carriers to better manage risk and prevent fraud, AI can be leveraged to deliver transformative solutions.
The question is how.
Once you address the key challenges and have a strategy about what business problem or opportunity AI is going to address, the recipe to create an AI engine using off-the-shelf tools and their extensions is simple.
So What Are The Challenges?
What is AI? Well, that’s the first challenge.
Given the breadth of its applications, AI can often be misunderstood.
Machine learning, computer vision, chatbots, natural language processing (NLP) and robotics are but a subset of AI applications.
An understanding and appreciation of the possibilities of AI is absolutely critical before determining a true AI solution to address a business need.
This knowledge will also help to address the elephant in the room: Is AI going to replace me in my job?
The mature AI solutions we have today will significantly enhance the productivity and efficiency of a human. AI should be viewed as a solution that, when paired with a human, will multiply value so that one plus one equals three.
The other challenge to address is data.
The effectiveness of any AI solution is dependent on the quality of data the solution requires to process. It is as simple as garbage in, garbage out.
However, combined with business expertise, there are mature commercial solutions available to cleanse and manage data. While cleansing and managing could take time and effort, this is a solvable problem.
Last but not least, prioritizing the use of AI can be a challenge. The limited time and availability of skilled resources are connected challenges, but once the true potential of AI is realized, this should be a non-issue.
Now, The Recipe For A DIY AI Engine
When it comes to AI solutions, one can start with simple use cases and implement a solution using out-of-the-box offerings. As an example, let us take the holy grail of any business: having a happy customer.
Now let's look at a recipe using AI, as there are multiple ways to address this even within AI.
Further breaking down the business problem, let’s take a situation wherein the customer is to be offered the best experience in their life cycle with the insurer irrespective of the channel through which they communicate.
The AI engine will be comprised of the following three layers integrated through extensions and, of course, some development: the experience layer, the orchestration layer and the execution layer.
The experience layer: The touch points with the customer should be made available on the channel of their choice – mobile, web, interactive voice response (IVR) or voice assistant. Now if we combine the touch points with cognitive services from Amazon, Google or Microsoft (an SE2 enterprise partner), we will get NLP so the customer can speak as if they were talking to a person. We can also use language translation to remove any barriers and a Q&A service that can be integrated with a knowledge bank. These cognitive services also come with several features to derive insights from something as simple as web analytics or as sophisticated as sentimental analysis.
The orchestration layer: This will be the core processing engine that is insights-driven and has the capability to self-learn over time-based on the type and content of the transactions going through it.
This layer will also execute actions leveraging underlying services. A combination of cognitive services -- machine learning libraries, event aggregators and a persistence layer.
The execution layer: A combination of robotics, service for straight-through processing and metric analysis will make up this layer. While the bots can automate manual steps and user interface flow in the back end, application programming interface (API)-enabled capabilities could automate end-to-end execution.
Tools for service metric monitoring and analysis combined with the sentiment analysis in the experience layer can provide valuable insights to continuously enhance the customer experience.
There are several mature AI technologies offering standard out-of-the-box capabilities.
These are apt for insurers to leverage for their business needs and to get started on simple yet impactful use cases. At the same time, these technologies should be viewed as possible enablers of new business opportunities and capabilities that don't necessarily exist today.
For insurers to remain competitive and to future-proof the way they do business, they must address these challenges and take advantage of their existing foundational data management capabilities or business process as a service (BPaaS) providers in order to think outside of the box in delivering customer-centric solutions.
