Predictive Engine for Transaction Term Success
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Solution Overview
Problem
Negotiated transactions involving multiple parties often become protracted due to the difficulty in agreeing on terms, as existing methods lack efficiency in predicting the likelihood of success and suggesting acceptable terms, leading to wasted time and energy in renegotiations.
Innovation Solution
A machine-learning trained predictive engine is used to analyze prior transaction data, determining the likelihood of success for candidate transactions and suggesting alternate terms or items to increase the chances of agreement among all parties involved.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated analysis of candidate terms is implemented using machine learning, then the predictability and speed of transaction term determination is improved, but the complexity of the system increases
Solution Approach 1:
The machine learning model is trained in advance on historical transaction data to learn patterns and predictors of successful transactions. This preliminary training phase allows the system to quickly evaluate candidate terms during actual transactions without requiring complex real-time analysis, thereby improving productivity while managing system complexity.
Solution Approach 2:
The predictive engine acts as an intermediary between transaction parties by analyzing candidate terms and providing recommendations on likely successful terms. This intermediary function automates the analysis process, improving transaction term determination speed while containing complexity within the predictive engine module rather than distributing it across the entire transaction system.
2Reliability
If multiple negotiation rounds are conducted to reach agreement on terms, then the likelihood of finding acceptable terms increases, but the time and energy consumed increases
Solution Approach 1:
Historical transaction data is analyzed in advance to identify patterns and predictors of successful transactions. This preliminary analysis creates a knowledge base that guides term selection during negotiations, increasing the likelihood of agreement on the first attempt and reducing the need for multiple negotiation rounds, thereby saving time while maintaining reliability.
Solution Approach 2:
The system incorporates feedback from historical transaction outcomes to continuously improve its predictions of successful terms. By learning from past successes and failures, the predictive engine provides increasingly accurate recommendations that提高 agreement likelihood while reducing negotiation time through data-driven insights.
Data Source
AI summary
A computer-implemented method for using a machine-learning trained predictive engine to predict failures includes receiving electronic prior transaction data corresponding to a plurality of prior successful transactions and a plurality of prior unsuccessful transactions, and training a machine learning predictive engine based on the plurality of prior successful transactions and the plurality of prior unsuccessful transactions. Electronic transaction data may be received, the electronic transaction data being associated with a user, an item, and candidate transaction terms, the electronic transaction data being associated with a candidate transaction. The machine learning predictive engine may determine a likelihood of success of the candidate transaction based on the electronic transaction data, and display the likelihood of success of the candidate transaction.