A typical claim process comprises of the following steps: initiation, assessment, adjudication, and settlement. Each of these steps impacts the claim experience for a policyholder or representatives of a policyholder.
Simultaneously, each step has a multi-dimensional impact on the insurance carrier as well as any intermediaries that may be involved. The claim process entails a number of decision points that include operations, risk management, settlement amount and loyalty.
For each claim decision step, data points are generated and are available for insurers. The available data on claims varies with the line of business, the size of the business and the defined claim process. A typical claim process encompasses data across structured and unstructured formats.
Analysis of these data sets has the potential to impact business dimensions such as loss ratio, claim settlement ratio, risk profiling and so on. Since the claim data is long-term in nature, it is a perfect fit for the development of predictive models that can drive optimized claims analytics.
Aureus' claims analytics solution empowers an insurer to aggregate structured and unstructured data sets across each step and drives impact using both predictive and point-of-decision frameworks.
CRUX can model claim analytics for each insurer based on the key factors that specifically impact their business. CRUX can also work with multiple systems involved in the claims cycle and process large amounts of structured and unstructured content recorded historically.
By using advanced predictive analytics and machine learning, CRUX helps organizations develop better customer risk profiles and cuts down the total time in claims settlement.
Better claims settlement turnaround times make the customer experience easier and hassle-free, while at the same time making it tough for fraudulent claims to pass through.
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