Vehicle Deal Structuring Using ML and Alternate Vehicle Matching
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Solution Overview
Problem
The vehicle purchasing process is complex and inefficient due to inaccurate loan estimates and a short transaction window, leading to frustration for both dealers and customers, with a risk of lost sales if data is not provided rapidly and accurately.
Innovation Solution
A system utilizing machine learning models to generate deal structures that optimize customer and lender acceptance, incorporating vehicle and customer data to recommend products and services, and identify alternate vehicles, enhancing profitability and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional loan estimation methods are used, then the process is simpler to implement, but the accuracy of loan estimates deteriorates due to incomplete information
Solution Approach 1:
The patent introduces a third-party data aggregation service as an intermediary to collect and provide comprehensive customer financial data. This mediator enables accurate loan estimates without requiring the dealer to build complex data collection systems, resolving the contradiction between measurement precision and device complexity.
Solution Approach 2:
The patent replaces manual data collection and traditional estimation methods with automated machine learning models that process comprehensive financial data. This substitution enables high-precision loan estimates while simplifying the overall system operation through automation rather than complex mechanical data gathering processes.
2Measurement precision
If comprehensive data collection is implemented, then the accuracy of deal structure determination improves, but the transaction time increases
Solution Approach 1:
The patent performs preliminary data collection and customer financial profile creation before the actual vehicle transaction. By aggregating comprehensive financial data in advance through the third-party service, the system enables rapid deal structure determination during the transaction window without requiring time-consuming data collection at the point of sale.
Solution Approach 2:
The patent replaces manual financial analysis with automated machine learning models that instantly process comprehensive financial data to determine optimal deal structures. This automation eliminates time-consuming manual calculations while maintaining high accuracy, resolving the contradiction between measurement precision and time loss.
3Productivity
If rapid data processing is implemented, then the transaction speed improves, but the completeness of data analysis deteriorates
Solution Approach 1:
The patent collects and pre-processes comprehensive financial data in advance through the third-party aggregation service, so that all necessary information is ready before the transaction. This preliminary action enables the system to process complete data rapidly during the transaction window without sacrificing data completeness for speed.
Solution Approach 2:
The third-party data aggregation service acts as an intermediary that comprehensively collects and validates financial data before providing it to the dealer's system. This mediator ensures data completeness is maintained while enabling rapid processing, as the heavy data collection and validation work is performed in advance by the intermediary service.
4Reliability
If accurate customer financial analysis is performed, then the deal structure optimization improves, but the system complexity increases
Solution Approach 1:
The patent uses a third-party data aggregation and analysis service as an intermediary to handle complex financial data processing. This mediator provides reliable deal structure optimization through specialized expertise and tools, allowing the dealer's system to remain relatively simple while achieving high reliability in deal optimization.
Solution Approach 2:
The patent replaces complex in-house financial analysis systems with automated machine learning models provided by the third-party service. This substitution achieves reliable deal structure optimization through sophisticated algorithms without requiring the dealer to maintain complex analytical systems, thereby improving reliability while controlling system complexity.
Data Source
AI summary
Methods for generating a deal structure is provided. A customer identifier associated with a customer interested in a vehicle is received. A financial data associated with the customer identifier and a vehicle data associated with the vehicle are determined. A plurality of recommended vehicles that are similar to the vehicle are determined based on the vehicle data associated with the vehicle. A deal structure metric is determined for each recommended vehicle of the plurality of recommended vehicles. At least one recommended vehicle is filtered from the plurality of recommended vehicles that based the deal structure metric. A vehicle recommendation to the customer is provided. The vehicle recommendation includes the vehicle information associated with the at least one recommended vehicle, one or more product or services associated with the at least one recommended vehicle, and the loan parameters associated with the at least one recommended vehicle.