The insurance industry, a cornerstone of economic stability in the modern age, is currently in the midst of a transformative paradigm shift. With its roots extending back to the dawn of capitalism, insurance has always been a necessity, providing a safety net for individuals and businesses alike. Yet, the traditional manual processes of insurance underwriting and claims management are poised to be redefined by the integration of artificial intelligence and Big Data.
Historically, insurers have relied on a triad of information sources when reviewing claims - claimant data, employer records, and medical information. This triad, while proven and dependable, has its limitations. Manual claims review is a time-consuming and resource-intensive process, where the challenge lies in separating eligible claims from ineligible ones.
To meet this need, Owl.co has introduced a new perspective to the traditional triad - a sprawling network of publicly available data. This additional perspective acts as a powerful resource sorting through 260 Million person records, 220 Million Company records, along with billions of publicly available data sources , providing a wealth of external evidence that can supplement the existing data sources to uncover information previously unavailable to insurers.
This innovative application of machine learning to insurance claims management shows promise in reducing not just the cost and time associated with claims review, but also the potential for bias. Rather than relying on a few controversial factors, insurers can now harness a wider array of data sources. This wealth of diverse data lends a new level of depth and nuance to risk assessment practices.