Predicting Vehicle Depreciation via Card Transaction Data
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Solution Overview
Problem
Financial institutions lack the ability to accurately determine the present value of a customer's vehicle without customer input, making it difficult to offer relevant auto financing products.
Innovation Solution
A system with machine learning capabilities that analyzes card transaction data to predict vehicle depreciation and present value by calculating miles driven, using factors like fuel expenses and vehicle information, and adjusts these calculations based on real-world data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the auto financing arm collects and retains more information about the customer's vehicle, then the ability to determine present value and offer relevant financing products improves, but the complexity of data collection and management increases
Solution Approach 1:
The patent uses card transaction data as an intermediary source to indirectly determine vehicle usage patterns and present value. Instead of directly collecting vehicle information, the system analyzes fuel purchase transactions, geographic location data, and temporal patterns from the customer's card account to infer mileage and vehicle value, thereby avoiding complex direct data collection while achieving accurate valuation
Solution Approach 2:
The system continuously monitors card transaction data and updates vehicle present value estimates in real-time based on changing usage patterns. As new transaction data becomes available, the machine learning models refine their predictions of mileage accumulation and depreciation, providing ongoing feedback that improves valuation accuracy without requiring additional direct data collection efforts
2Measurement precision
If the system waits for customer input to determine vehicle value, then data accuracy improves, but the responsiveness and timeliness of financing product offerings deteriorates
Solution Approach 1:
The system performs preliminary calculations of vehicle present value continuously in the background using available card transaction data, so that when a customer is ready to consider financing products, the valuation is already complete and ready for immediate presentation. This eliminates waiting time while maintaining accuracy through ongoing data analysis
Solution Approach 2:
The system automatically determines vehicle present value without requiring customer input or action. The machine learning models self-service the valuation task by independently analyzing card transaction patterns, calculating mileage, applying depreciation factors, and generating present value estimates autonomously, thereby eliminating delays associated with customer response while maintaining data-driven accuracy
3Measurement precision
If the system uses detailed card transaction data to calculate miles driven, then the accuracy of depreciation prediction improves, but the complexity of data processing and analysis increases
Solution Approach 1:
The patent segments the complex task of depreciation prediction into distinct analytical components: (1) identifying fuel-related transactions from card data, (2) calculating miles driven from transaction patterns and geographic data, (3) applying vehicle-specific depreciation factors based on make/model/year, and (4) synthesizing these segments into a final present value estimate. This segmentation simplifies processing by handling each aspect separately using targeted algorithms
Solution Approach 2:
The system changes parameters dynamically based on vehicle characteristics and usage patterns. Different depreciation rates are applied based on vehicle type, age, and observed mileage patterns from card data. The machine learning models adjust their parameters and weighting factors as they learn from accumulated transaction data, improving accuracy while adapting to different vehicle profiles without requiring completely different processing approaches
Data Source
AI summary
Various embodiments are directed to a system or platform with machine learning capabilities configured to accurately predict in real-time a depreciation factor of a vehicle associated with a customer and further accurately predict a present value of the vehicle based at least in part on card transaction data associated with the customer. Based on one or more factors, such as a determination that the present value of the vehicle falls below a predefined threshold value, one or more auto financing products may be generated and provided to the customer by the system.