Powering the modern life insurance carrier with advanced AI and data implementation
Powering the modern life insurance carrier with advanced AI and data
Powering the modern life insurance carrier with advanced AI and data
Part 2 of 4: New product implementation
Welcome back to our white paper series about how to use AI and data throughout the new business and underwriting processes. In the first white paper we discussed the importance of product design and the benefits of using AI. In this white paper, we will examine how to deliver on the vision of your new product through your technology implementation. We will discuss how to move from concept to reality effectively and efficiently by determining the right capabilities, automation, and optimized workflows for this goal. To meet customer expectations, carriers are looking to accomplish more with less and still achieve superior results. Companies are turning to new data sources and digital technologies that allow them to access new and alternative data rather than rely on historical data. This unprecedented amount and variety of data enables carriers to quickly make decisions while maintaining acceptable risk levels related to mortality and morbidity outcomes.
Moving from concept to reality.
As we discussed in the previous white paper, new product design is critical for succeeding in todays’ market.
In that paper we focused on using AI to build a data repository, create a baseline dataset, and conduct analyses to design new products more quickly and with a higher level of confidence in financial viability. With your dataset and assumptions in place, the next step is to conduct a detailed review of your existing technology systems to move your product from concept into production.
The technology systems used by carriers have been established based on historical application and determination processes that have endured for decades. These time-consuming, labor-intensive activities may require paper-driven, in-person interviews conducted by an agent with the customer; the collection of a customer’s medical records; medical tests on paramedically collected samples such as blood and urine; and a detailed review conducted by an underwriter. In many cases this data is collected and saved in the system in a human readable format such as an image or PDF, even if the underlying information originated as structured data.
With this sudden shift to new datadriven underwriting paradigms the entire application, data collection and decisionmaking process has changed, and yet a carrier’s existing IT infrastructure often is still based on these historical processes.
Review the capabilities of your existing IT operations.
To ensure a successful launch of your new product, carriers must examine their existing IT infrastructure to determine if their current system can handle requirements needed for data collection, analysis and real-time decision making. Organizations will need to look at the entire process from application to issuance to match each step with a corresponding technology requirement. This process review should be from the perspective of both the customer and the underwriter.
The carrier should walk through the flow of a customer completing an online application to determine how the process will work and if you have all the systems and rules in place. The goal is to make the application process easy for the customer while providing enough information for underwriting to make a sufficient determination.
Here are some questions to consider:
- Does an electronic application already exist?
- Does your organization already have an underwriting workbench serving as a single control panel for the entire underwriting process? Do the e-application and workbench already work together?
- Can requirements be ordered at the beginning of the application process as part of the submission stage or will that happen afterwards through the workbench?
- Can you extract data from traditional data sources, such as APS’s and using AI/NLP tools convert them to structured formats to process through automation?
- Can the application data be submitted to a rules engine that will automatically accept a certain risk amount and decline those that do not meet the rules?
- not fall within those rules? Are they declined or can they automatically be sent to underwriters for review?
- Do you get deep analytics to show you the performance of the underwriting process end-to-end and highlight areas for improvement?
Future-proofing your system
Carriers also need to review their technology systems in a more general sense. Important questions to ask here include how effectively does your system manage queues? How are workflows updated? Will a developer need to be involved or does your system offer a low/ no-code platform, so operational teams can modify business rules? What is the status of the system’s data protection, especially sensitive medical data?
The quick-changing nature of today’s market also raises the question of adaptability. An IT system today needs to have the performance and scalability to meet both immediate and future needs, such as the ability to integrate with external data sources and rapid modification of associated underwriting rules and processing, flexibility in deployment model (i.e., cloud enabled/ native), and have an actively evolving roadmap.
With the increasing availability of numerous data sources, including Electronic Health Records and scores it is essential that any system has the ability not just to manipulate and manage that data to get automated outcomes, but also be able to provide comprehensive analytics. Using past performance data and combining that with current models, any modern system should be able to provide machine learning driven analytics and be able to extrapolate predictive outcomes. These capabilities are moving from the horizon and rapidly becoming part of the underwriting landscape and EXL has developed a variety of solutions in this space that are already helping to streamline automation, by using AI driven data extraction tools for medical data sources (APS’s) through to machine learning driven insights within their underwriting (LDS) platform.
To commit to a new operational model, carriers must also be sure that their investment will yield an improvement in performance. Any new technology solutions or integrations should be flexible enough to future-proof this investment.
To commit to a new operational model, carriers must also be sure that their investment will yield an improvement in performance.
IT system changes are challenging and take significant time and investment. Not all systems need to be tackled at once. It becomes essential to work with a solution provider that has a modular and flexible system that allows for faster integration with legacy IT systems and that can be integrated in steps. A chosen solution partner should have practical experience in this type of implementation and be able to offer guidance on best practices to reach a successful outcome within given timelines and budgets. Select a partner that has the size and scalability to be there for you not just today, but into the future.
As underwriting evolution takes the industry forward into the unknown, it will be essential that solutions also provide real-time feedback and dashboards to track performance against the newly established baseline and assumptions. EXL’s market leading LDS platform provides real time data through machine learning driven analytics and is already demonstrating that the technology is ready for this next stage and we will take a deeper dive into these aspects in the next paper of our series and how to respond and adjust to the new information to keep your product portfolio performance on track..
The drive to offer a real-time insurance product
By building a data paradigm and implementing AI-driven baselines, carriers can dramatically improve efficiency, allowing them to underwrite cases faster and achieve their desired outcomes. The best solutions will enable smart requirement ordering and allow for modern technology such as data extraction tools using AI/NLP to streamline traditional underwriting data sources such as APS’s and the adoption of EHRs, that will minimize underwriting requirement spend on the front end while automating the mundane underwriting decisions at a high rate while focusing the workload for the remaining cases which will allow for the handling of increased case volumes using fewer resources and reducing operational costs.
With the right combination of product design, implementation strategy, the appropriate technology solutions, and a trusted partner to drive this operational transition, carriers can achieve their vision of delivering a life insurance product that meets both market and business demands.
EXL Service offers AI-driven software solutions and professional expertise to life insurance clients to implement data extraction, data analytics and actuarial support so clients can more accurately define and build new products with anticipated financial results. EXL’s underwriting solutions offer the options to build your life insurance ecosystem at your pace and adopt the power of AI. From bringing in new channels for distribution through its conversational AI tools, right through to data extraction to automate APS’s all the way through to groundbreaking machine learning driven insights in its LDS platform that can utilize heatmap analytics to allow the clients to continuously monitor and improve processes.
Read next in the series: Powering the modern life insurance carrier with advanced AI and data
Part 3: Product launch and distribution—coming soon
Part 4: Product monitoring and adjustments—coming soon