ML Loan Matching System for Commercial Real Estate
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Solution Overview
Problem
Purchasing property, especially commercial real estate, often requires obtaining a loan, but finding the right lender can be challenging for both borrowers and lenders.
Innovation Solution
A method and system using machine learning to match loan requests with suitable lenders and loan offers with potential purchasers, by receiving and processing loan requests and offers, and recommending and distributing them to multiple lenders or purchasers based on their criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual loan matching process is used, then lenders can carefully evaluate each loan request, but the time required to close deals increases significantly
Solution Approach 1:
The patent introduces a machine learning-based intermediary system that acts as a mediator between lenders and loan requests. This system automatically matches loan requests with suitable lenders by analyzing multiple criteria simultaneously, thereby reducing the time required for manual evaluation while maintaining matching accuracy. The intermediary process handles the time-consuming aspect of evaluation, allowing human lenders to focus on final decision-making.
Solution Approach 2:
The system performs preliminary filtering and matching of loan requests with lenders before human intervention. By pre-processing loan requests and identifying suitable lenders based on multiple criteria, the system reduces the workload for human lenders and accelerates the overall matching process. This preliminary action ensures that lenders only need to review pre-selected matches rather than evaluating all requests manually.
2Reliability
If multiple lenders are contacted sequentially, then each lender can be thoroughly evaluated, but the overall process time increases
Solution Approach 1:
The patent segments the lender evaluation process into multiple independent parallel streams. Instead of sequentially contacting lenders one by one, the system divides the task by simultaneously evaluating multiple lenders against the same loan request using machine learning algorithms. This segmentation allows thorough evaluation of each lender while maintaining high overall productivity through parallel processing.
Solution Approach 2:
The system dynamically adjusts the matching process by simultaneously engaging multiple lenders in parallel evaluations. The machine learning model can dynamically allocate loan requests to multiple suitable lenders at once, rather than following a fixed sequential order. This dynamic approach maintains selection quality by considering multiple options simultaneously while significantly improving matching efficiency.
3Measurement precision
If a comprehensive set of lending criteria is collected from multiple lenders, then better matching accuracy is achieved, but the complexity of the system increases
Solution Approach 1:
The patent implements a universal machine learning platform that handles multiple lending criteria and lender profiles through a single integrated system. This multi-functional system can process diverse criteria from different lenders simultaneously, achieving comprehensive matching accuracy without requiring separate systems for each lender's specific requirements. The universal platform simplifies complexity by providing a unified approach to handling varied lending criteria.
Solution Approach 2:
The system manages complexity by dynamically adjusting and weighting multiple parameters within the machine learning model. Instead of creating separate systems for each criterion, the platform changes parameters such as criterion weights and matching thresholds based on the specific loan request and lender preferences. This parameter-based approach allows comprehensive criterion evaluation while maintaining system simplicity through a single adaptable model.
Data Source
AI summary
Disclosed herein are methods and computer systems for fulfilling loan requests and loan offers using machine learning. Lender-borrower and lender marketplace computing platforms are disclosed that allow a mixture of artificial intelligence and human intervention. Each of the platforms allow for various levels of automation and human interaction: including up to fully automated processes. In one example a method of fulfilling loan requests using machine learning includes: (1) receiving a loan request for purchasing property. (2) receiving lending criteria from multiple lenders. (3) recommending. using a machine learning algorithm. one or more of the multiple lenders for servicing the loan request based on the loan request and the lending criteria of each of the multiple lenders, and (4) distributing the loan request to at least some of the one or more of the multiple lenders.