ML Labeling Platform for Property Damage Assessment
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Solution Overview
Problem
Current machine learning models for assessing property damage in insurance claims are often inaccurate and require costly manual adjustments, leading to inefficient and expensive data curation processes.
Innovation Solution
A computer-implemented method and system that uses a machine learning model to analyze property damage in media, determines the confidence level of the assessment, and if below a threshold, avails the media for manual review by qualified individuals to update the damage assessment, and automatically generates work orders for service providers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning models are used to assess property damage automatically, then productivity is improved, but measurement precision deteriorates
Solution Approach 1:
The system dynamically adjusts the assessment process by evaluating confidence levels for each damage assessment and automatically routing cases that fall below thresholds to human reviewers, while confidently handled cases proceed automatically. This dynamic adaptation resolves the contradiction by optimizing both speed and accuracy on a case-by-case basis.
Solution Approach 2:
Human reviewers serve as an intermediary layer between automatic ML assessment and final damage determination. The system introduces this intermediate step selectively for low-confidence cases, allowing ML to handle high-confidence cases automatically while human reviewers correct or validate uncertain assessments, thus improving overall precision without completely eliminating automation.
2Measurement precision
If manual review is used to correct model inaccuracies, then measurement precision is improved, but loss of time increases
Solution Approach 1:
Instead of applying manual review to all cases, the system applies it partially only to cases where the ML model's confidence level falls below a predetermined threshold. This selective application of manual review maintains precision where needed while minimizing time loss by avoiding unnecessary human intervention in confidently handled cases.
Solution Approach 2:
The system dynamically determines the level of human intervention required based on confidence level assessments. By adjusting the proportion of cases requiring manual review based on model performance and confidence metrics, the system optimizes the balance between precision improvement and time efficiency.
3Measurement precision
If manual parameter adjustment is performed to improve model accuracy, then measurement precision is improved, but productivity deteriorates
Solution Approach 1:
The system enables self-service model improvement by automatically collecting data from human reviewer corrections and using this feedback to retrain and improve the ML model without requiring manual parameter adjustment. The model serves itself by learning from the discrepancies between automated assessments and human expert reviews, thus improving precision while maintaining productivity.
Solution Approach 2:
The system implements a feedback loop where human reviewer assessments of low-confidence cases are fed back into the training data to improve the ML model. This automated feedback mechanism allows the model to learn from errors and improve accuracy continuously without requiring manual parameter tuning, thus resolving the contradiction between precision improvement and productivity maintenance.
Data Source
AI summary
Systems and methods for dynamically assessing property damage by determining whether and how to leverage a crowdsourcing marketplace are provided. According to certain aspects, a server computer may receive a set of media depicting property damage, and may analyze the set of media using a machine learning model to estimate a type and amount of the property damage. The server computer may also determine whether and how to leverage a set of additional individuals to provide a set of assessments for the property damage and, based on the set of assessments provided by the set of additional individuals, the server computer may automatically facilitate a work order request to address the property damage.