AI Claim Automation With Predictive Assessment for Fast Settlement
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Solution Overview
Problem
Insurance claim handling processes are slow and inefficient, leading to customer dissatisfaction and increased processing costs, as customers expect faster turnaround times due to increased internet access and reduced patience for delays.
Innovation Solution
Implementing a self-service claim automation system using artificial intelligence (AI) that integrates with IoT devices and augmented reality to facilitate quick claim processing, including customer identity validation, predictive impact assessment, and real-time coverage determination, enabling customers to file and settle claims within minutes through a user-friendly interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional manual claims handling processes are used, then accuracy and customer service quality can be maintained through human adjusters, but processing speed is slow and turnaround time is long
Solution Approach 1:
The system enables automated self-service claims processing where the AI engine independently validates customer identity, assesses claim impact, determines coverage, and authorizes payments without human adjuster intervention for standard claims. This automation dramatically increases processing speed while reducing the need for complex human oversight structures.
Solution Approach 2:
The patent replaces the mechanical system of manual human adjuster review with an AI-based automated decision engine that uses machine learning models, predictive analytics, and rule-based systems to evaluate claims, determine coverage, and authorize payments. This substitution enables faster processing while maintaining consistency and accuracy.
2Productivity
If more claims adjusters and support staff are employed to handle increased claim volumes, then processing capacity increases, but operational costs increase
Solution Approach 1:
The automated AI engine processes claims independently without requiring proportional increases in human adjuster staffing. The system handles increased claim volumes by scaling computational resources rather than human resources, thereby increasing productivity while controlling operational costs associated with employee benefits, training, and management.
Solution Approach 2:
The system changes the fundamental parameter of claims processing from human labor-intensive operations to automated computational operations. This parameter change enables the organization to handle varying claim volumes by adjusting computational capacity rather than hiring additional staff, thereby improving productivity while avoiding the linear cost increases associated with expanding human workforces.
3Loss of time
If customers are provided with faster claims processing, then customer satisfaction and retention improve, but processing accuracy may be compromised in automated systems
Solution Approach 1:
The AI engine incorporates multiple feedback mechanisms including customer identity validation, predictive impact assessment scoring, and coverage rule verification that continuously check and verify automated decisions. This feedback loop ensures processing accuracy is maintained despite the speed of automation, building customer trust in the rapid automated process.
Solution Approach 2:
The system performs preliminary actions including customer identity validation, claim data verification, and coverage eligibility assessment before the main claims processing decision is made. These preliminary checks ensure that even though the overall process is fast, accuracy is maintained through pre-validated information and pre-screened candidates for automated processing.
Data Source
AI summary
Embodiments are disclosed for automatically processing a claim provided by a user. Responsive to receiving a notice of loss associated with a claim of a user, a set of customer identity validation data are collected. The set of customer identity validation data may be determined to meet a pre-defined identity validation criteria. Responsive to determining that the set of customer identity validation data meets the pre-defined identity validation criteria, current claim evaluation data for the claim may be accessed. A set of predictive impact assessment scores associated with the current claim evaluation data may be determined using a predictive model. The set of current claim evaluation data may be determined to meet pre-defined claim data criteria by comparing the predictive impact assessment scores with a set of impact assessment thresholds. Responsive to determining that the set of current claim evaluation data meets the pre-defined claim data criteria, a reactive action to the claim may be determined.