While Coordination of Benefits (COB) and fraud, waste and abuse (FWA) have been a part of the insurance industry for almost as long as there’s been an insurance industry, they’ve taken on a new level of importance for today’s health insurers. Outdated, inefficient and ineffective COB and FWA processes can result in higher payouts, increased administrative costs and decreased customer satisfaction. In today’s marketplace, insurers can no longer afford any of these outcomes.
Fortunately, big data analytics, machine learning and artificial intelligence (AI) can provide insurers with capabilities for COB and FWA cost recovery when they need them most. Here are five ways in which insurers can manage cost recovery efforts through the power of data analytics:
- Move from “pay and chase” to predictive. It’s much more efficient to handle COB before claims are processed and payments are made than to chase recovery afterward. Data analysis and scoring can identify consumers who either have or are likely to have additional insurance coverage and prioritize those cases.
- Don’t rely on your customers to inform you of potential COB application. Even with the best of intentions, customers don’t always provide all the information insurers need to manage situations where multiple insurers bear potential responsibility for some portion of a claim. Integrating third-party data sets and public-domain information into an insurer’s analytics increases the opportunities to independently identify consumers who are most likely to have secondary coverage.
- Automate the small-claim review process. Every insurer has a cutoff point at which a claim is too small for its COB or FWA processes to deal with, simply because the potential recovery at stake is less than it would cost to identify, investigate and process it. But thanks to automated data analysis and AI-driven scoring, insurers are finding substantial aggregate savings in those smaller claims, to the point where they far outweigh the cost of implementing the cost of implementing the COB analysis tools.
- Most issues are with the patient, not with the claim. Too many insurers take a claim-centric approach to COB analysis, when most of the actual issues are centered around the member and not the individual claim. An analytics-driven COB function can use a combination of internal and third-party data sets to identify the consumers who are the source – or potential source – of COB challenges.
- Use AI and machine learning-driven scoring to focus the efforts of your investigators and underwriters. While COB has always worked hand-in-hand with FWA claims adjustment functions, the same analysis and scoring factors that power COB also make FWA investigations far more efficient and effective.
Data is readily available to insurers, and with the right strategies and tools, it can be leveraged to save costs and improve efficiency across the board. By implementing a data-driven approach to cost recovery and COB, insurers can take the next step towards achieving digital transformation.