Vehicle Recommendation System Using Financial Data Analysis
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Solution Overview
Problem
Users face difficulties in determining the affordable price of a vehicle for purchase, as they need to visit multiple dealers and negotiate prices, and struggle to assess which vehicles they can realistically afford based on monthly payment amounts that do not account for other loans or obligations.
Innovation Solution
A computer-implemented method and system that analyzes transactional data to determine purchasing power, compares it with vehicle sales data to provide personalized vehicle recommendations, and uses a trained machine learning model for bidding data to suggest suitable vehicles based on affordability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users visit multiple vehicle dealers and negotiate prices face-to-face, then they can potentially find affordable prices, but the process becomes time-consuming and complex
Solution Approach 1:
The system introduces a third-party intermediary (the vehicle recommendation system) that mediates between the user and multiple dealers. Instead of the user directly visiting and negotiating with multiple dealers, the system aggregates dealer information, analyzes user financial capabilities, and provides curated recommendations, thereby reducing the time and effort required while maintaining access to multiple pricing options
Solution Approach 2:
The system performs preliminary analysis of user financial data (income, expenses, debts) and pre-calculates affordable vehicle price ranges before the user needs to make a decision. This preliminary action of assessing purchasing power and filtering vehicles by affordability eliminates the need for users to visit multiple dealers without having a clear idea of their budget, significantly reducing the time required for the purchasing process
2Loss of information
If users rely on publicly available sources for vehicle pricing information, then the information is easily accessible, but the information is insufficient and inaccurate for determining actual affordable prices
Solution Approach 1:
The system continuously refines its pricing recommendations by analyzing user financial data and comparing it with vehicle pricing information. It provides feedback to the user about their actual purchasing power based on real financial data (income, expenses, debts) rather than relying on static publicly available information, thereby improving the accuracy and personalization of affordable price determination
Solution Approach 2:
The system changes the parameters used for pricing analysis from publicly available general information to personalized financial data specific to each user. By analyzing individual user financial parameters (income, expenses, debts) and combining them with vehicle pricing data, the system achieves precise and accurate determination of affordable prices tailored to each user's specific financial situation
3Device complexity
If users base vehicle affordability decisions solely on monthly payment amounts, then the calculation is simple, but it fails to account for other loans and obligations
Solution Approach 1:
The system segments the affordability calculation into multiple components: it separately analyzes income, expenses, existing debts, and monthly payment capacity. Instead of relying on a single simplified monthly payment figure, it breaks down the financial picture into distinct segments, allowing for a comprehensive and accurate assessment that accounts for all financial obligations while keeping the overall process manageable through structured analysis
Data Source
AI summary
A computer-implemented method for determining a reward associated with one or more transactions of a user may comprise obtaining travel data of the user via a device associated with the user, wherein the travel data includes travel dates of the user; obtaining, via one or more processors, exchange rate data based on the travel data of the user; determining, via the one or more processors, a value of the reward associated with the one or more transactions of the user during the travel dates based on the exchange rate data; transmitting, to the user, a notification indicative of the reward associated with the one or more transactions; and causing the reward associated with the one or more transactions to be directed to a financial account associated with the user.