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Claims Analytics

Critical moment of truth for the Insurance business

Insurance claims are tricky. By using effective claims analytics models, insurers can predict claims and identify potential fraud in advance as well as optimize claim outs and deliver a much superior customer experience.


A typical claim process comprises intimation, registration, handling and settlement as substeps. Each of these sub-steps impact the claim experience for a policy-holder or representatives of a policyholder. Simultaneously, each substep has multi-dimensional impact for an Insurance carrier and as well intermediaries involved, if any. The claim process subsumes number of decision points that include operations, risk management, settlement amount and Loyalty.


For each claim decision point, data points are generated and available with Insurers. The available data on claims varies with line of business, size of the business and the defined claim process. A typical claim process comprises data across structured and unstructured formats. Analysis of these datasets has 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 ideal for the development of predictive models that can drive optimized claims analytics.


Aureus Claims Analytics solution empowers the Insurer to aggregate structured and unstructured datasets across each substep and drive impact using both predictive and point of decision frameworks. CRUX can model claim analytics for each insurer based on the key factors that impact their business, and is able to work with multiple systems involved in the claims cycle and process large amounts of structured and unstructured content recorded historically.


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