Machine-Learning Loss Prediction for Automated Claim Intake
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Solution Overview
Problem
Existing SaaS providers face inefficiencies in claim processing, particularly in insurance claims, due to time-consuming manual procedures and suboptimal communication with policy holders, leading to frustration and delays.
Innovation Solution
A computing system that utilizes machine-learning and AI to optimize claim processes by generating AI prompts for large language models, performing guided content capture, and implementing dynamic scripting and engagement monitoring to streamline information gathering, reduce computing resources, and automate negotiation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual procedures are used for claim processing, then communication with policy holders can be maintained, but claim processing time increases and efficiency decreases
Solution Approach 1:
The system enables automated self-service claim processing where the machine-learning model independently performs information gathering, content capture, and negotiation without requiring manual human intervention at each step, thereby dramatically reducing processing time while maintaining communication capabilities
Solution Approach 2:
Manual mechanical procedures are replaced with an automated machine-learning system that uses AI prompts and large language models to handle claim processing tasks, transitioning from human-driven manual operations to automated intelligent processing
2Measurement precision
If manual information gathering is performed, then accurate claim data can be collected, but computing resources and processing time are consumed
Solution Approach 1:
The system performs preliminary guided content capture and information gathering before full claim processing begins, using AI prompts to pre-collect necessary data in an organized manner, which reduces both the time needed and the computational resources required during subsequent processing stages
Solution Approach 2:
Large language models serve as intermediaries between the system and various data sources, enabling accurate information gathering through natural language interactions while optimizing the efficiency of data collection and reducing direct computational overhead
3Productivity
If automated procedures are implemented, then claim processing speed increases, but communication quality with policy holders may deteriorate
Solution Approach 1:
The system incorporates feedback mechanisms where the machine-learning model continuously monitors and adjusts its communication with policy holders based on their responses and engagement levels, ensuring that automated interactions maintain quality while achieving high processing speeds
Solution Approach 2:
The communication approach is made dynamic and adaptive, allowing the system to adjust its interaction style, tone, and level of detail based on the specific policy holder and situation, thereby maintaining high communication quality despite automated processing
Data Source
AI summary
Embodiments include a computing system, computing device, computer-implemented method and non-transitory computer readable medium for providing a machine-learning model for optimized loss prediction. In embodiments, incident data is received, corresponding to an incident involving a property of a user, and based on the incident data, a total loss prediction is generated, the total loss prediction indicating a damage repair amount for the property.