Report Analysis Platform Using ML for Issue Detection
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Solution Overview
Problem
Existing methods for auditing reports, such as randomly sampling reports, have low accuracy and are time-consuming, struggling to scale with increasing report volumes, resulting in a significant majority of reports going unaudited and wasting resources.
Innovation Solution
A report analysis platform using machine learning models to process historical reports and audits, determining multi-entity profiles, supervised, and unsupervised model features to identify issues, and generating scores for quick and efficient detection of issues across all reports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If random sampling of reports is used for auditing, then the auditing process is simple to implement, but the accuracy of issue detection is low
Solution Approach 1:
The patent replaces manual random sampling with an automated machine learning system that uses trained models to analyze report features and predict issues. The system substitutes mechanical review processes with computational algorithms that automatically score and flag reports based on learned patterns from historical data.
Solution Approach 2:
The system transforms the auditing approach by changing from uniform random sampling to differential analysis based on multiple parameters including report features, historical audit results, and risk scores. The ML model dynamically adjusts which reports to audit based on calculated risk profiles rather than random selection.
2Productivity
If manual auditing of all reports is performed, then the accuracy of issue detection is high, but the time consumption and resource waste are significant
Solution Approach 1:
The system performs partial auditing by focusing resources on high-risk reports identified through ML scoring rather than reviewing all reports equally. The risk-based prioritization ensures that auditing efforts are concentrated where they are most needed, processing high-risk reports thoroughly while lower-risk reports receive minimal or no manual review.
Solution Approach 2:
The ML model enables self-service auditing where the system automatically identifies and flags suspicious reports without requiring manual intervention for every report. The automated risk assessment and flagging system handles the initial screening, allowing human auditors to focus only on cases that require their expertise.
3Quantity of substance
If the volume of reports increases, then the organization processes more data, but the existing auditing methods cannot scale effectively
Solution Approach 1:
The patent replaces manual auditing mechanics with automated ML-based processing that can handle increasing volumes without proportional increases in human resources. The system scales computationally rather than requiring linear increases in auditor headcount, maintaining efficiency even as report volumes grow to millions of documents.
Data Source
AI summary
A device may receive data that is related to historical reports associated with an organization, historical audits of the historical reports, and individuals associated with the historical reports. The device may determine a multi-entity profile for the data. The multi-entity profile may include a set of groupings of the data by a set of attributes included in the data. The device may determine, using the multi-entity profile, a set of supervised model features for the historical reports. The device may determine, using the multi-entity profile, a set of unsupervised model features for the historical reports independent of the historical audits. The device may determine, utilizing a model, a score for a report. The device may perform one or more actions.