ML Reserve Estimate Prediction for Faster Insurance Claim Handling
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Solution Overview
Problem
Existing Software as a Service (SaaS) providers face inefficiencies in computational resource usage and claim processing times, particularly in handling insurance claims, which are time-consuming and frustrating for policy holders and providers alike.
Innovation Solution
A computing system optimized for SaaS providers that utilizes artificial intelligence and machine learning to streamline claim processes, including guided content capture, dynamic scripting, and LLM summarization, to enhance information gathering and automate negotiations, reducing computational and processing times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional claim processing methods are used, then claim processing accuracy is maintained, but claim processing time is excessive and computational resource usage is high
Solution Approach 1:
The patent replaces traditional manual claim processing mechanisms with machine learning models and automated systems. Specifically, ML models analyze claim data, determine reserve estimates, and automate negotiations, substituting human-intensive mechanical processes with computational algorithms that process claims faster and more efficiently.
Solution Approach 2:
The system enables automated self-service claim processing where the ML-driven platform independently handles claim intake, analysis, reserve determination, and negotiation without requiring continuous human intervention. The system serves itself by automatically learning from historical data and improving its processing capabilities over time.
2Measurement precision
If manual reserve estimation is performed, then accuracy can be maintained through expert judgment, but processing time and resource consumption increase significantly
Solution Approach 1:
The patent substitutes manual expert judgment with machine learning models that have been trained on historical claim data. These ML models automatically analyze claim characteristics, historical outcomes, and relevant factors to generate reserve estimates with consistent accuracy, replacing the complex human expert assessment process with computational algorithms.
Solution Approach 2:
The system transforms the reserve estimation process by changing from subjective human judgment parameters to objective data-driven parameters. The ML models process structured claim data, historical reserve information, and outcome data to generate estimates based on statistical patterns rather than human intuition, thereby standardizing and automating the assessment criteria.
3Reliability
If comprehensive claim information is gathered manually, then negotiation accuracy is improved, but communication time and network bandwidth consumption increase
Solution Approach 1:
The patent replaces manual information gathering and communication processes with automated ML-driven systems. The ML models analyze claim data, predict negotiation outcomes, and automatically conduct negotiations through digital platforms, substituting human-to-human communication with automated computational interactions that require minimal network bandwidth while maintaining negotiation reliability.
Solution Approach 2:
The system creates digital copies and representations of claim information that can be rapidly processed and shared without additional network bandwidth consumption. The ML models work with structured data copies of claim files, historical records, and negotiation parameters, eliminating the need for repeated transmission of original documentation during negotiation processes.
Data Source
AI summary
A computing system can accumulate a dataset comprising claim files that have been processed to completion. The system can train a machine learning model using the dataset to predict optimal reserve estimates for claim events. The system can receive information corresponding to a claim event. The system further executes the trained machine learning model on the information corresponding to the claim event to generate an optimized reserve estimate for the claim event.