Augmenting Online Banking Statements with Item-Level Receipt Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current online banking systems lack the ability to accurately account for individual items in e-commerce transactions, as they typically aggregate costs without providing item-level data, making it difficult for customers to review and manage their expenditures.
Innovation Solution
A computer-implemented method that parses graphical user interfaces (GUIs) to detect receipts, extracts item-level data, and matches this information with transaction data to provide augmented transaction statements, allowing for accurate tracking and verification of purchases within online banking systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If transaction statements aggregate multiple items into a single line item, then the statement structure remains simple, but item-level detail is lost making it difficult for customers to review expenditures
Solution Approach 1:
The patent segments the transaction information by separating aggregated transaction data from detailed item-level receipt data. The system divides the statement into summary-level transaction lines and expandable detailed item lists, allowing customers to view only necessary detail without overwhelming the overall statement structure.
Solution Approach 2:
The patent adds a new dimension to transaction statements by incorporating item-level data that was previously unavailable. This transforms the flat, aggregated transaction view into a multi-dimensional structure that includes both summary and detailed item information, enabling customers to drill down from transaction level to item level.
2Measurement precision
If a capture agent parses all GUIs continuously, then receipt detection accuracy improves, but processing time and resource consumption increase
Solution Approach 1:
The capture agent performs preliminary actions by pre-configuring merchant-specific parsing logic and criteria before actual receipt detection occurs. This allows the system to quickly identify and parse only relevant GUI elements from known merchants without analyzing every GUI element in detail, reducing processing time while maintaining accuracy.
Solution Approach 2:
The system applies partial parsing actions by focusing only on specific GUI elements and merchants that are likely to contain receipts, rather than parsing all GUIs completely. This selective approach processes only the necessary portion of GUI data to achieve accurate receipt detection without the overhead of comprehensive parsing.
3Reliability
If the system stores detailed associations between receipts and transactions, then transaction verification accuracy improves, but data storage requirements increase
Solution Approach 1:
The system extracts only the essential matching fields (merchant name, transaction amount, date) needed for verification and stores these as association keys. The full receipt and transaction details remain in their respective source systems, so the association table stores minimal reference data rather than duplicating complete records, reducing storage requirements while maintaining verification accuracy.
Data Source
AI summary
Disclosed embodiments include systems and methods for improving the accuracy of online banking systems. In various embodiments, the system includes a user device capturing receipt information from one or more e-commerce application and a server device for associating captured receipt information with transaction data. The system may include a browser extension for facilitating automatic capture of receipt information and a matching module for associating receipt information with transaction data. In various embodiments, transactions displayed in transaction statements within an online banking application are augmented with item level data including product name, price, and vendor for each product included in a transaction.