Machine Learning Claims Analysis System
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Solution Overview
Problem
Insurance claim processing is complex and often results in missed reimbursement opportunities due to policy holders not fully understanding their coverage or submitting claims to the wrong insurer.
Innovation Solution
A machine-learning driven system that standardizes policy and claim information, uses trained models to analyze and group claims by event type, and predicts which insurer is likely to cover specific claims, providing recommendations for submission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual claim submission process is used, then insured users can submit claims, but reimbursement rates are low due to confusion about coverage and wrong insurer selection
Solution Approach 1:
The system automatically analyzes policy coverage information and claim information to identify suitable insurers and generate submission recommendations without requiring the insured user to manually understand complex policy details or make decisions about which insurer to submit to
Solution Approach 2:
The processor acts as an intermediary between the insured user and multiple insurers, analyzing the data and recommending the most appropriate insurer for claim submission based on standardized policy and claim information
2Adaptability or versatility
If multiple insurance policies with different coverage types are held, then comprehensive coverage is provided, but insured users cannot understand the minutiae of each policy and miss reimbursement opportunities
Solution Approach 1:
The system separately obtains and analyzes policy coverage information for each insurance policy, then compares this segmented information against claim information to identify which specific policy covers which specific claim, preventing missed reimbursement opportunities
Solution Approach 2:
The processor automatically performs the analysis and comparison of policy and claim information that would be too complex for manual processing, replacing the need for insured users to understand policy minutiae
3Measurement precision
If standardized schema and machine learning models are used, then prediction accuracy is improved, but data processing complexity increases
Solution Approach 1:
A single standardized schema is designed to handle multiple types of policy information and claim information from different insurers, allowing the same machine learning model to process diverse data uniformly and improve prediction accuracy without proportionally increasing system complexity
Data Source
AI summary
A data processing system for insurance claims analysis and adjudication implements obtaining policy coverage information for each of a plurality of insurance policies and insurance claim information associated with a plurality of insurance claims associated with an insured user, analyzing the insurance claim information using a first machine learning to obtain event-related claim grouping information; analyzing the event-related claim grouping information and the standardized policy information using the second machine learning model to obtain coverage prediction information comprising a prediction, for each event of the one or more events, identifying a respective insurance policy of the plurality of insurance policies likely to cover the one or more claims associated with each event, the second machine learning model being trained using second training data formatted according to the standard schema; and providing, via a network connection, the coverage prediction information to a computing device associated with the insured user.