Regulatory challenges and policy complexity call for powerful automation
Traditional business models are under pressure from new market entrants, as technology offers new ways of packaging and delivering insurance products. To succeed in this environment, insurance businesses use WorkFusion to reduce operational costs, enhance service, build brand value and contribute to the bottom line.
Build long-lasting customer relationships by streamlining and accelerating the customer experience, using all available information to tailor and personalize solutions with accurate, compliant data.
Improve speed and accuracy when processing and validating policy changes by mining data, matching key facts and making automated decisions using machine learning models.
Automating Insurance Claims Processing
The major operational hurdle in the insurance industry is claims handling. Individuals take out policies in the event that something unexpected happens, and when it does, they file a claim with their insurance provider. The claims service defines the relationship between the insurer and the insured in ways that no other interaction can. The process involves getting the all the claim information into the system, adjudicating whether or not to pay the claim and then actually issuing the payment to the customer. This process involves some brute force work in terms of getting the claims into the system and processing the payment. However, it also involves some cognitive analysis to determine whether or not the claim should actually be paid. Throughout this whole process, a workflow is required to move the claim from step-to-step in the fastest manner possible to improve the customer experience. Even the most efficient operations teams struggle to deliver an ideal customer experience.
Complex: claim to policy matching and validation against riders, addendums, etc.
Cross-system: involves multiple core systems
WorkFusion is used to mine knowledge bases, auto-validate policies, match key facts from the claim to the policy and make automated decisions using machine learning models, and can automatically transmit data into the system of record for downstream payment.
Digitization and extraction of unstructured source documents Core system application integration with RPA bots ML-enabled decision making and adjudication
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