Recurring Payment Reminder System Using Transaction History Analysis
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Solution Overview
Problem
Consumers face challenges in managing recurring payment transactions, as existing systems fail to provide timely reminders and recommendations for future transactions, leading to inefficiencies and increased workload, especially for busy individuals.
Innovation Solution
A system that identifies recurring payment transactions through tagging and analyzes historical data to provide personalized reminders and recommendations for future transactions, including product suggestions, merchants, and spending amounts, using a processing server that stores transaction data, executes queries, identifies recurring patterns, and generates recommendations for consumers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If consumers manually set reminders using calendar services, then they can be notified of upcoming purchases, but they must still decide what to purchase without guidance, consuming additional time and effort
Solution Approach 1:
The system automatically analyzes transaction history data to generate purchase recommendations without requiring consumer intervention. The processing server autonomously identifies recurring transactions, determines appropriate purchase timing, and provides specific product/merchant suggestions based on historical patterns, allowing the system to serve itself rather than requiring manual calendar management
Solution Approach 2:
The system performs preliminary analysis of transaction data and generates purchase recommendations in advance of the actual purchase decision moment. By pre-processing historical transaction patterns and preparing suggested purchases before the consumer needs to decide, the system eliminates the need for last-minute research and decision-making
2Adaptability or versatility
If consumers manually research and decide what to purchase for recurring transactions, then they can make informed decisions, but the process becomes time-consuming and difficult for busy consumers
Solution Approach 1:
The system continuously monitors and analyzes consumer transaction history data, using this feedback to generate increasingly accurate purchase recommendations. By leveraging historical purchase patterns, merchant preferences, and spending behaviors, the system adapts to consumer preferences over time, providing personalized suggestions that improve in quality with each transaction analyzed
Solution Approach 2:
The processing server acts as an intermediary between the consumer's transaction history and future purchase decisions. Rather than requiring consumers to directly research and analyze their own spending patterns, the system intermediates by automatically processing historical data and translating it into actionable purchase recommendations, simplifying the decision process while maintaining adaptability
3Productivity
If a third party system automatically identifies recurring transactions and provides recommendations, then consumer time and effort are saved, but technological challenges arise in capturing, analyzing, and delivering personalized recommendations at scale
Solution Approach 1:
The system segments the complex task of purchase recommendation into distinct functional modules: a data capture component that collects transaction history, a processing server that analyzes patterns and identifies recurring transactions, and a recommendation engine that generates personalized suggestions. This segmentation allows each component to specialize in specific tasks, managing complexity while maintaining high productivity
Solution Approach 2:
The processing server is designed as a multi-functional system that simultaneously performs data reception, transaction pattern analysis, recurring transaction identification, and recommendation generation. By consolidating these functions into a single universal platform that can handle multiple consumer accounts and transaction types, the system achieves scalability without proportionally increasing complexity
Data Source
AI summary
A method for providing electronic purchase reminders includes: storing communication information and a plurality of transaction data entries, each transaction data entry corresponding to a processed payment transaction and including transaction data, a transaction date, and one of a plurality of category tags; storing, for each category tag, a recurring time period; identifying a subset of transaction data entries that includes a common category tag; identifying a most recent transaction data entry of the subset o based on the transaction date, where a difference between the transaction date included in the most recent transaction data entry and a present date is at least the recurring time period associated with the common category tag; generating a purchase recommendation based on the transaction data included in each transaction data entry in the subset; and transmitting the generated purchase recommendation to a computing device based on the communication information.