Automated Contract Risk Assessment Using Sentence-Level ML Classification
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Solution Overview
Problem
Traditional risk assessment processes for documents are prone to human errors, inconsistencies, and slow turn-around times, requiring expert analysis and being costly, especially when assessing large volumes of documents quickly.
Innovation Solution
An automated risk assessment method using a risk assessment engine that maps document sentences to risk categories, identifies risk-associated language, and generates assessments based on risk criterion documents, allowing for accurate, consistent, and quick risk evaluations, with the ability to incorporate additional data and update risk categories easily.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual risk assessment processes are used, then expert analysis quality is maintained, but assessment time and costs increase significantly
Solution Approach 1:
The patent creates a digital copy of the expert risk assessment process by training a machine learning model on expert-labeled documents. The model learns to replicate expert decision-making patterns, enabling automated assessments that mirror human expert quality without requiring actual experts to perform each assessment manually.
Solution Approach 2:
The patent replaces the mechanical system of human experts performing manual analysis with an automated machine learning system. The ML model processes documents through computational algorithms, substituting human cognitive processes with automated text analysis, risk category classification, and criterion-based evaluation.
2Reliability
If expert analysts are used for risk assessment, then assessment quality is maintained, but costs increase due to high expertise requirements
Solution Approach 1:
The patent captures and replicates expert knowledge by training the ML model on documents labeled by experts. This creates a reusable digital artifact that encodes expert judgment criteria, allowing consistent application of expert-level analysis without incurring repeated expert labor costs.
Solution Approach 2:
The patent transforms the assessment process from requiring human expert parameters (skills, experience, judgment) to using computational parameters (algorithmic rules, trained model weights, criterion thresholds). This parameter transformation enables automated systems to achieve reliability previously only attainable through expensive human expertise.
3Productivity
If manual risk assessment is performed, then nuanced understanding of document context is achieved, but processing speed decreases for large document volumes
Solution Approach 1:
The patent segments the document analysis process into distinct components: sentence-level risk category classification, identification of risk-associated language, mapping to risk criteria, and aggregate risk scoring. This segmentation allows the system to process large volumes efficiently while maintaining contextual understanding through structured analysis at each stage.
Solution Approach 2:
The patent adds a structural dimension to the analysis by organizing risk assessment around predefined risk categories and criteria hierarchies. This dimensional framework allows the system to process documents systematically, maintaining contextual accuracy through multi-layered classification while enabling parallel processing of multiple documents.
4Measurement precision
If traditional risk assessment methods are used, then comprehensive human judgment is applied, but inconsistencies arise due to individual analyst variations
Solution Approach 1:
The patent creates a standardized digital copy of the risk assessment methodology that is applied uniformly to all documents. The ML model uses consistent trained parameters and criterion thresholds for every assessment, eliminating the variability introduced by different human analysts while maintaining the quality of expert judgment.
Data Source
AI summary
Techniques are provided for the automated risk assessment of a document. In one embodiment, the techniques involve mapping, via a risk assessment engine, one or more sentences in a first document to one or more risk categories, identifying, via a classification engine, risk-associated language of the one or more sentences based on the one or more risk categories, mapping, via a risk assessment engine, the risk-associated language of the one or more sentences to one or more risk criterion of a risk criterion document, and generating, via a risk assessment engine, a first risk assessment based on the one or more risk criterion of the risk criterion document.