Efficient claims processing has become a top challenge for insurance companies as the cost and complexity of healthcare continues to rise. Redis Enterprise provides the building blocks for modern digital claims processing platforms with in-memory database performance, real-time event processing, and AI/ML model serving.
Insurance companies receive millions of claims each day from healthcare professionals through a vast array of software platforms. Errors in the claims process lead to countless phone calls and resubmittals that waste time and raise the cost of care.
While electronic claims submission is growing in popularity, many claims-processing entities use multiple internal and external systems that require days between processing steps. Payers and providers are now looking for ways to integrate different claims platforms, or build their own end-to-end systems.
Traditional claims processing systems that run on legacy hardware and rely on inflexible approval rules can’t process claims quickly, and are significantly harder to update over time. Modernizing these systems with cloud infrastructures, event-driven architectures, and AI inferencing means insurance claims can be efficiently handled in real time.
Inefficient claims processing forces healthcare professionals to waste time chasing payments instead of spending time on patient care, leading to worse health outcomes for patients and higher insurance costs. Redis Enterprise provides the real-time performance and modern data models needed to power real-time claims inquiries, instant member lookups, auto claims adjudication, and more.
Events can represent the steps of the claims process without requiring tightly coupled application logic. Use Redis Enterprise to trigger data processing in response to updates in your claims processing workflow, and as a store for real-time events and streams.
A claims status inquiry often involves expensive queries across dozens of different systems. Redis Enterprise can be used as an event store and search engine for more efficient claims status inquiries, or as an in-memory database for best performance.
While AI and machine learning models typically need to query reference data stored in a separate database, Redis Enterprise lets you serve deep-learning models directly where data is stored to enable faster claims auditing and fraud detection.