Insurance decisioning: Bridging automation and customer experience
How can insurers keep their customers happy and connected while creating a more streamlined experience?
Despite its seemingly easy to understand definition, automation in the insurance industry often means different things to different people. For some, automation covers the entire process, from FNOL to settlement. For others, it means automating sub-processes in the claim lifecycle such as damage estimates or payments. The reasons an insurer will explore automation opportunities are equally as varied – like improving speed to respond, reducing workload for staff or enablement of a digital initiative. And because of this, insurance automation initiatives may fall short in delivering what customers truly want – a seamless, simple, great experience when dealing with their insurer.
A big part of the problem is that the act of automating processes is relatively easy. But which process can be automated? Which process should be automated? And how can you really tell the difference?
That’s where the concept of insurance decisioning comes in. Decisioning is the act of arriving at a conclusion or choice after taking in information surrounding something being considered.
We know all too well that the insurance industry is bounded by guardrails that define the space in which insurers can operate. Insurance Department regulations, statutes and case law define everything from what services can be offered to how much insurers can charge for a policy. To complicate things further, insurance companies face myriad “somethings” being considered in almost every aspect of serving their customers. In this complex and ever-changing environment, how can insurers be confident they are making the best decisions possible on behalf of the business, its policyholders, and its employees?
What carriers need are technology solutions that deliver impactful decisions at scale and improve the business metrics that will drive differentiation in the marketplace. One area where insurance decisioning is readily visible is in a claim organization. While handling a claim, adjusters are constantly faced with decisions, both large and small. They must determine what is needed to evaluate the circumstances of the loss, define the resulting damages, establish a value for the damages, communicate their findings and ultimately resolve the claim. Adjusters continually determine what is needed to make well-founded decisions based on facts presented and the other investigative information and insights gathered. Combine this with their experiences in evaluating and resolving similar claims to determine the best path forward and insurance decisioning comes to life.
In essence, insurance decisioning is the leverage of information and insights plus intellectual and contextual understanding of the loss to arrive at a determination that delivers the optimal outcome.
Insurers have pursued many options for simplification and optimization through automation initiatives in recent years. A number of these are based on processes where if/then scenarios can be completed without requiring human intervention. These were commonly solved through analytics, business rules and RPA/IPA solutions to improve an insurer’s cost to serve. These are integral enabling automation capabilities but fall short of insurance decisioning.
These investments are a good start, but aren’t addressing most scenarios facing a claim organization, let alone when complex and conflicting loss scenarios surface. These initiatives have focused on creating efficiency and cost take-out. The opportunity is to advance an insurer’s automation strategy to introduce and apply intellectual and contextual understanding by exploiting AI-based insurance decisioning. This enables advancing claims and sub-processes with human-like knowledge and expertise based on an insurer’s best performers. Taking this next step will accelerate your automation strategy and impact indemnity, expense and customer experience. Following are a few examples of where outcome improvements can be achieved:
- Claim decision acceleration
- Persistent accuracy and quality
- Contextually appropriate insights delivered to adjusters
- Customer self-serve enablement
- STP advancement
- Leakage improvement
Now, let’s turn our sights on how insurance decisioning can deliver the seamless, simple and great experience customers are seeking. Insurers have been focused on, and delivering, excellent customer experience as evidenced by numerous industry satisfaction surveys. To challenge or displace those in the leader position, or for those holding leader positions to retain them, additional value and positive experiences for customers must be delivered consistently. The six preceding bullets each can impact positive customer experience (e.g., faster resolution, fewer touches, reduced errors, etc.) as well as settlement outcome. It is through solutions that create positive impact on multiple outcome measures where an insurer can achieve the maximum ROI.
The elements of value and positive experiences for customers can be defined by insurers, but the difficulty comes with consistently delivering against those expectations. The cyclical claim volume swings, combined with auto claim volume anticipated to return to pre-COVID levels will create challenges for executing the best of plans. Something more is needed to catalyze the plan for value and positive experience into sustainable results.
AI-based insurance decisioning can learn and perform with the human-like knowledge and expertise of your best adjusters. One of the greatest attributes of this type of solution is that it doesn’t tire from surges in claims, masses of data or high number of new documents. This infuses a persistent and predictable delivery resource to drive customer experience outcomes continually for a claim organization.
Claim organizations will continue to be charged with delivering optimal outcomes in loss cost, claims expenses and customer experience as measured through their decisions. The best organizations will leverage more data and information to create contextually appropriate insights to improve their accuracy and claim resolution cycle time. Introducing AI-based insurance decisioning catalyzes the knowledge of the best claim handlers to achieve this at scale with persistency and predictability. Such a transformation generates positive impacts on loss costs and claims expenses, creating the opportunity for claim handlers to invest in the “somethings” important to their customer.
Jim Sorrells is a Shift Technology P&C Insurance Industry Subject Matter Expert.