Machine Learning Model for Dynamic Contract Pricing Assessment
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Solution Overview
Problem
Companies face challenges in assessing and optimizing contract performance due to a lack of transparency and inefficient processes for determining favorable contract parameters, leading to inefficiencies in contract negotiations and compliance tracking.
Innovation Solution
A computer-implemented method using machine learning to analyze contract data and market conditions, training a model with historical contract data to predict optimal terms for potential contracts, thereby enhancing contract negotiation and compliance tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual processes are used to assess contract performance and determine favorable parameters, then companies can maintain simplicity in implementation, but the process efficiency and transparency remain poor
Solution Approach 1:
The patent replaces manual mechanical assessment processes with an automated machine learning system. The ML model automatically analyzes contract data, market conditions, and performance metrics to generate assessments and recommendations, eliminating the need for manual review while significantly improving efficiency and transparency.
Solution Approach 2:
The patent introduces an intermediary machine learning system that acts as a bridge between raw contract data and actionable insights. This intermediary processes and structures the data, providing transparent explanations of assessment criteria and recommendations, thereby improving both efficiency and interpretability.
2Loss of information
If companies implement comprehensive contract portfolio analysis, then transparency into drivers of performance is improved, but the complexity of data processing and analysis increases
Solution Approach 1:
The patent segments the contract assessment process into distinct analytical components: contract term analysis, market condition evaluation, performance metric assessment, and recommendation generation. Each component is handled by specialized ML models or algorithms, making the overall complex system manageable and interpretable through modular processing.
Solution Approach 2:
The patent implements feedback mechanisms where the ML model continuously learns from actual contract outcomes and performance data. This feedback loop refines the model's understanding of performance drivers over time, improving transparency into what factors actually influence contract success while automating the complex data processing required.
3Reliability
If traditional methods are used for setting portfolio strategy and renegotiating contract terms, then implementation remains simple, but the overall performance optimization is limited
Solution Approach 1:
The patent applies preliminary action by using the ML model to predict optimal contract terms and identify potential performance issues before contracts are finalized or renewed. The system analyzes historical data and market conditions to pre-determine favorable parameters, allowing companies to negotiate from an informed position and optimize performance proactively rather than reactively.
Solution Approach 2:
The patent leverages parameter changes by using the ML model to dynamically adjust contract parameters based on real-time market conditions and historical performance data. The system identifies optimal pricing, duration, and other contractual parameters by analyzing how these parameters have impacted performance in similar contracts, thereby improving optimization reliability through data-driven parameter selection.
Data Source
AI summary
Systems and methods for using machine learning to dynamically assess contract parameters are disclosed. According to certain aspects, an electronic device may train a machine learning model using real-world pricing and contract data, access parameters associated with a potential contract for an entity, and analyzing, using the machine learning model, the accessed parameters. Based on the analysis, the machine learning model may output a set of potential terms for the potential contract. Data indicative of this output may be availed to the entity to be used in negotiating and executing the contract, among other uses.