Financing Structure Generation Through Seller Clustering
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Solution Overview
Problem
Existing systems face inefficiencies in generating financing structures for products due to varying methodologies among entities, leading to a slow, error-prone process and network bottlenecks from multiple requests and responses.
Innovation Solution
A learning engine uses unsupervised machine learning, such as K-means clustering, to group sellers into clusters based on attributes impacting final financing structures, optimizing financing structures for both sellers and buyers by predicting acceptable terms that meet lender and buyer preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple requests and responses are transmitted to generate financing structures, then financing structures can be generated for different entities, but computational resources are consumed and network bottlenecks are created
Solution Approach 1:
The system performs preliminary clustering of entities based on their attributes and behaviors before generating financing structures. By pre-grouping entities into clusters with similar characteristics, the system avoids processing each entity individually through multiple iterative requests, thereby reducing computational resource consumption and network traffic while maintaining customized financing structure generation for each entity type
Solution Approach 2:
The system creates a universal clustering model that can handle multiple entity types (car dealerships, home builders, real estate agents, retail stores) through a single processing framework. This multi-functional approach allows the system to generate financing structures for diverse entities using one unified system rather than requiring separate processing chains for each entity type, improving overall productivity
2Reliability
If multiple iterations are performed to generate financing structures, then financing structures can be optimized for entities, but the process becomes slow and error-prone
Solution Approach 1:
The system performs preliminary clustering of entities based on their attributes and behaviors before generating financing structures. By pre-grouping entities into clusters with similar characteristics, the system avoids processing each entity individually through multiple iterative requests, thereby reducing computational resource consumption and network traffic while maintaining customized financing structure generation for each entity type
Solution Approach 2:
The system incorporates feedback mechanisms where clustering results and financing structure generation outcomes are used to refine and update cluster models continuously. This feedback loop allows the system to learn from previous iterations and improve accuracy over time without requiring multiple manual iteration cycles, thereby reducing processing time while maintaining or improving financing structure accuracy
Data Source
AI summary
Described herein is a system for generating financing structures. A learning engine may extract data sets associated with sellers of various products. The learning engine may be trained using the data sets. The learning engine may identify a subset of dimensions that cause a change in a determination of a final price for a given product. The learning engine may compute a value for each of the sellers with respect to each dimension. The learning engine may group the sellers into different clusters. The learning engine may generate using a model, including the subset of dimensions. The learning engine may receive a request to generate a financing structure for a specified product sold by a specified seller. The learning engine may generate financing structures for the specified product sold by the specified seller based on the generated model.