Blockchain Lending Negotiation Assistant
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Solution Overview
Problem
Current systems lack automated or semi-automated tools to assist borrowers in negotiating lending products, particularly in real-estate transactions, leading to an imbalance in negotiation skills between lenders and borrowers, with no existing solutions to facilitate effective negotiation processes.
Innovation Solution
A system utilizing a conversation interface and machine-learning models to assist borrowers in negotiating lending products on a distributed ledger network, where smart contracts and cryptographic tokens are used to memorialize agreements, and a lending product recommendation engine generates recommendations based on borrower input, leveraging blockchain technology for secure and transparent transactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If automated negotiation tools are introduced to assist borrowers, then negotiation effectiveness and fairness are improved, but system complexity increases
Solution Approach 1:
The patent introduces an automated negotiation assistant as an intermediary system between borrowers and lenders. This assistant uses machine learning models to analyze borrower objectives, generate negotiation strategies, and provide real-time guidance during lending product negotiations. The system acts as a mediator that balances the negotiation power between parties without requiring direct complex interactions between borrowers and lenders themselves.
Solution Approach 2:
The negotiation assistant enables borrowers to serve themselves by providing automated recommendations and strategies. The system processes borrower input, applies machine learning models to determine objectives, and generates personalized negotiation guidance without requiring external expert intervention. This self-service approach improves accessibility while managing complexity through automation.
2Measurement precision
If personalized recommendations are generated using machine-learning models, then lending product suitability is improved, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-processing borrower input and pre-determining borrower objectives using machine learning models before the actual negotiation process begins. The negotiation assistant analyzes borrower needs, financial状况, and preferences in advance to generate pre-tailored recommendations, reducing the time required during the actual negotiation interaction.
Solution Approach 2:
The system implements feedback mechanisms where the machine learning models continuously learn from negotiation outcomes and borrower responses. This feedback loop refines recommendation accuracy over time while optimizing processing efficiency. The system adjusts its analysis depth and recommendation generation based on feedback from previous interactions, balancing precision and speed.
Data Source
AI summary
Methods and processes can include negotiation assistance to the borrower in the context of attempting to secure a lending product offered by a lender within a blockchain environment. In some embodiments, the system may receive borrower input within a conversation interface of an application and convert the input to identify borrower information. Further, the system may apply a first machine-learning model to the borrower input to determine at least one borrower objective. Finally, the system may generate a lending product recommendation including an explanation for the lending product determination.