Predictive Analysis System for Contract Term Validation
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Solution Overview
Problem
The process of reviewing contracts for deals is slow and complex, often failing to identify all errors in terms and conditions, which can lead to misunderstandings or malfeasance between parties.
Innovation Solution
A predictive analysis system using machine learning to parse and compare contract documents with a repository of contract terms, determining difference parameters and validation criteria to assess the acceptability of terms, with user feedback improving the system's accuracy over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual contract review is performed, then comprehensive error identification may be achieved, but the process becomes slow and complex
Solution Approach 1:
The system performs preliminary automated analysis of contract documents before human review, extracting key terms, clauses, and potential errors in advance. This preliminary action filters out obvious issues and prepares structured data, reducing the time required for comprehensive manual review while maintaining error identification completeness.
Solution Approach 2:
An automated machine learning system acts as an intermediary between the contract document and human reviewers. The intermediary performs initial error detection, term extraction, and compliance checking, then presents filtered and organized findings to human users for final verification, thereby reducing overall review time while maintaining high reliability.
2Productivity
If automated systems are used for contract review, then processing speed increases, but accuracy in identifying all errors decreases
Solution Approach 1:
The system incorporates feedback loops where user corrections and validations of automated analysis results are fed back into the machine learning model. This continuous feedback improves the system's accuracy over time, allowing it to maintain high productivity while progressively enhancing error identification accuracy through learned patterns from real-world corrections.
Solution Approach 2:
The contract review process is segmented into multiple specialized automated modules, each handling specific aspects such as term extraction, clause validation, compliance checking, and risk assessment. This segmentation allows each module to focus on specific error types with high accuracy, and the combined results provide comprehensive error identification at scale.
3Reliability
If detailed validation criteria are applied, then compliance assurance improves, but system complexity increases
Solution Approach 1:
The system employs a universal validation framework that handles multiple compliance requirements and validation criteria through a single integrated engine. This multi-functional approach allows detailed validation of various contract aspects (legal compliance, risk profiles, standard terms) without proportionally increasing system complexity, as the core validation mechanism remains consistent across different validation types.
Data Source
AI summary
Systems and methods may utilize a predictive analysis model to analyze a contract or other document. A system may parse a document and/or a repository of information associated with the document. The system may identify one or more terms in the document and corresponding terms in the repository. The system may determine a difference parameter between a first term extracted from the document and a second term extracted from the repository. The system may determine whether the difference between the first term and the second term, represented by the difference parameter, is likely to be acceptable to the user using a predictive analysis model. The system may report a validation parameter indicating a level of acceptability associated with the difference. User feedback on the accuracy of the predictive analysis model is used to train, modify, and improve the predictive analysis model.