Claims Management

Streamlining the Claims Process with Data Analytics and AI

Goal

Process Improvement → Faster Assessment & Fraud Detection → Customer Satisfaction → Increased Revenue and ROI

Challenges

Oftentimes, situations that arise from loss events awake the insuring populace towards the need for protective measure. However, claims are noted as the most critical channels and a defining link that shape the overall perception of the customers towards their insurer. According to Capgemini, the claims processing is the mirror to the customer that enable the insurers’ drive at improving customers’ acquisition, expectation, retention and business’ insight for product enhancement and company’s profitability. The assessment and payment of the claim should be fast and accurate to ensure customer satisfaction and prevent issues like fraud. Manual processing with analysts and rule-based systems making choices in claims can slow down the process results in a poor customer experience.

Opportunities

Data-Driven AI which is ideal for automating repetitive processes, can streamline processing by scoring claims for issues like fraud . Meanwhile, allows claims with low probability of an issue to be processed automatically while higher probability claims are routed to investigators for review. Data-Driven AI models can also supply justification messages for claim denials speeding up analyst decision making, and can be helpful to customers by informing them of issues with their claim, giving them the opportunity to fix the claim for re-approval and payment.

Application Specific Services

AI.R1 Solution.

Anonymization and Information Retrieval Solution.
From paper to Information solution.
Covariance Team supports you to “read paper”, create automatically structured data bases and acquire Information using Machine Learning Technology.

Application Specific Services

AI.R2 Solution.

Analytics, Insights and Reporting.
From paper to Insights solution.
Covariance Team supports you to “read paper”, create automatically structured data bases and harvest insights using Machine Learning Technology.

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