ML Engine for Automated Contract Risk Prediction
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Solution Overview
Problem
Organizations face challenges in predicting the time and effort required to establish complex contracts and assessing the likelihood of contract performance due to unclear insights into contract characteristics such as parties, political conditions, and geographic locale, making it difficult to anticipate potential non-performance and plan accordingly.
Innovation Solution
A machine learning engine analyzes documents associated with past actions to determine critical events and generate models that predict risk values for pending contracts, incorporating external inputs like political and weather information, and updating these predictions based on changing conditions, while also providing reputation scores for involved parties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of contract characteristics is used, then organizations can assess contract performance likelihood, but it requires significant time and effort
Solution Approach 1:
The patent replaces manual mechanical analysis of contract characteristics with an automated machine learning system that processes contract documents and external data sources. The system uses natural language processing to extract features from contracts and applies trained models to predict performance likelihood, eliminating the need for manual review while maintaining or improving prediction accuracy.
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between contract documents and performance predictions. The model acts as a mediator that processes contract text, party information, and external data to generate risk scores and performance predictions, enabling automated decision-making without direct human analysis of each contract characteristic.
2Reliability
If comprehensive contract analysis is performed to predict performance likelihood, then risk assessment improves, but system complexity increases
Solution Approach 1:
The patent segments the contract analysis system into distinct functional components: document processing module that extracts contract features, data collection module that gathers external information, machine learning model that generates predictions, and reporting module that presents results. This modular architecture improves reliability through specialized processing while managing complexity through clear separation of concerns.
Solution Approach 2:
The patent creates a universal machine learning platform that handles multiple contract types, parties, and performance metrics through a single system. The system processes various document formats, integrates multiple external data sources, and generates comprehensive risk assessments, reducing the need for separate analysis tools for different contract scenarios.
3Productivity
If automated machine learning analysis is implemented, then prediction speed increases, but initial setup and data collection effort increases
Solution Approach 1:
The patent performs preliminary actions by pre-training machine learning models on historical contract data and pre-configuring data collection pipelines for external sources. The system establishes data gathering mechanisms in advance for party information, contract performance history, and external risk factors, enabling rapid analysis once deployment begins without requiring manual setup for each contract.
Solution Approach 2:
The patent implements self-service capabilities where the system automatically collects data from external sources, trains and retrains models on new data, and updates predictions without manual intervention. The automated system manages its own data collection, model maintenance, and performance optimization, reducing ongoing implementation effort after initial setup.
Data Source
AI summary
Embodiments are directed to managing documents over a network. A machine learning (ML) engine analyzes a plurality of documents associated with actions that were performed previously. The ML engine determines critical events associated with the performance of the actions based on the plurality documents. The ML engine generates ML models based on the critical events to compute risk values that may be associated with the critical events. In response to a request to compute risk values associated with pending actions, the ML engine determines documents that are associated with the pending actions based on the request. The ML engine determines the critical events associated with pending actions based on the documents. The ML engine employs the ML models to generate the risk values based on the documents and the critical events. The ML engine provides the risk values in response to the request.