AI Contract Risk Analysis System
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Solution Overview
Problem
Existing contract evaluation methods are inefficient and prone to human error, as they require manual analysis of lengthy documents, leading to potential gaps in understanding and increased risk of executing weak contracts due to ambiguous or missing clauses.
Innovation Solution
A system and method utilizing artificial intelligence and a virtual assistant-based chatbot interface for autonomous contract risk analysis, which detects and classifies clauses, calculates risk levels, and provides insights and alerts, thereby minimizing the likelihood of contract failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual analysis of contract documents is used, then human judgment and experience can be applied to evaluate clauses, but the process is time-consuming and prone to human error
Solution Approach 1:
The patent replaces manual mechanical analysis with an AI-based automated system. The machine learning model processes contract documents automatically, extracting clauses and calculating risk levels without human intervention, thereby eliminating time consumption and human error while maintaining assessment accuracy through sophisticated algorithms
Solution Approach 2:
The system performs self-service by automatically evaluating contracts without requiring human analysts. The AI model independently processes documents, identifies clauses, determines risk categories, and generates assessments, enabling the system to serve itself rather than requiring continuous human oversight for routine evaluations
2Reliability
If detailed cost/risk evaluation is conducted manually, then comprehensive analysis can be performed, but the complexity and susceptibility to human error increase
Solution Approach 1:
The patent replaces complex manual evaluation processes with an automated AI system that handles comprehensive analysis. The machine learning model systematically processes all contract clauses, applies risk assessment algorithms, and generates complete evaluations without the complexity and error-proneness of manual operations
Solution Approach 2:
The system segments the contract evaluation process into distinct automated steps: document processing, clause extraction, risk category determination, and assessment generation. This segmentation allows comprehensive analysis to be broken down into manageable automated tasks, reducing overall process complexity while maintaining completeness
3Productivity
If automated AI models are used for contract analysis, then speed and consistency of evaluation improve, but the ability to handle ambiguous clauses and gaps may be limited
Solution Approach 1:
The patent incorporates feedback mechanisms where the AI model processes contract clauses, determines risk categories, and the system can iterate through ambiguous sections. The model continuously refines its analysis based on the contract context, improving its ability to accurately identify ambiguous clauses while maintaining high evaluation speed through automated processing
Solution Approach 2:
The system changes parameters dynamically by adjusting the machine learning model's analysis depth and risk assessment criteria based on the complexity of clauses detected. This allows the automated system to adapt to ambiguous situations by modifying processing parameters rather than relying on fixed thresholds, thereby improving accuracy while maintaining productivity
Data Source
AI summary
A system and method of determining whether specific clauses in a contract have a likelihood of falling under a particular risk category is disclosed. The proposed systems and methods describe an autonomous digital assistant that accepts contract-related queries from end-users and generates conversational responses. For example, an end-user may request information about the contract's terms, and the system can automatically provide intelligent analytical responses that bypass the need to manually evaluate the various features of the document.