Adaptive Call Sub-Flows for Faster Insurance Claim Handling
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Solution Overview
Problem
Existing Software as a Service (SaaS) providers face inefficiencies in claim processing, particularly in insurance claims, due to time-consuming manual procedures and suboptimal communication with policy holders and providers, leading to frustration and delays.
Innovation Solution
A computing system that utilizes artificial intelligence and machine learning to optimize claim processes, including guided content capture, adaptive content flows, and automated negotiation, to streamline information gathering and reduce processing time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual procedures are used for claim processing, then communication flexibility is maintained, but processing time increases and efficiency decreases
Solution Approach 1:
The claim processing workflow is divided into distinct sub-flows (e.g., information gathering, document verification, negotiation, settlement) that can be independently executed. AI agents handle specific sub-flows autonomously while human agents handle others, allowing parallel processing and improved efficiency without complete automation.
Solution Approach 2:
An AI negotiator agent acts as an intermediary between the policyholder and the insurance company during settlement negotiations. This intermediary can autonomously communicate, negotiate terms, and reach agreements, reducing manual intervention while maintaining flexible communication protocols.
2Reliability
If comprehensive information gathering is performed, then claim accuracy improves, but time consumption increases
Solution Approach 1:
The system proactively requests and gathers necessary information at the beginning of the claim process, including policy details, incident information, and required documentation. This preliminary action ensures all necessary data is collected upfront, preventing delays later while maintaining comprehensive verification.
Solution Approach 2:
The system implements real-time feedback mechanisms where the AI agent monitors information completeness and automatically requests missing data. This feedback loop ensures comprehensive information gathering without manual intervention, maintaining accuracy while reducing time loss through automated tracking and prompting.
3Productivity
If multiple sub-flows are executed sequentially, then thoroughness is maintained, but overall processing time increases
Solution Approach 1:
The system dynamically determines the execution order of sub-flows based on claim type, complexity, and available information. Rather than fixed sequential execution, the workflow adapts by parallelizing independent sub-flows (e.g., document verification can proceed simultaneously with initial assessment) while maintaining proper coordination through a central workflow manager.
Data Source
AI summary
Based on an initial set of claim data for a claim event of a user, a computing system can generate an initial script and a content flow that includes the initial script for a call representative to utilize in a call session with the user, the content flow comprising a plurality of sub-flow groupings. Based on the user initiating a topic corresponding to a respective sub-flow grouping from the plurality of sub-flow groupings, the system can update the content flow to enable the call representative to hop to the respective sub-flow grouping within the content flow.