Electronic Payment Data Cleansing and Biller Scrubbing
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Solution Overview
Problem
Existing electronic bill payment systems face challenges in accurately routing payments due to discrepancies in data entry formats between consumers and billers, leading to unsuccessful electronic fund transfers and the need for paper checks, which are slower, less secure, and more polluting.
Innovation Solution
A method and system for data cleansing and biller scrubbing that involves standardizing payment data, using matching logic to identify correct billers, and advising clients on updated payment routes, implemented through a computer program product and system with processors and memory, facilitating the transition from paper checks to electronic payments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data entry formats are standardized, then payment routing accuracy is improved, but data processing complexity increases
Solution Approach 1:
The system performs preliminary data cleansing and standardization on payment data before routing attempts. By pre-processing the data to correct formatting issues, validate information, and standardize structures in advance, the system improves routing accuracy without adding complexity to the core routing logic itself.
Solution Approach 2:
The patent introduces an intermediary data cleansing layer between data entry and payment routing. This intermediary component handles the complexity of format standardization and validation, acting as a buffer that improves routing accuracy while isolating the routing system from processing complexity.
2Reliability
If matching logic is applied to identify correct billers, then electronic payment success rate is improved, but processing time increases
Solution Approach 1:
The system performs preliminary matching and validation of payment data against biller information before initiating the actual payment transfer. By pre-identifying the correct biller and validating data accuracy in advance, the system improves payment success rates while minimizing delays during the critical payment execution phase.
Solution Approach 2:
The matching logic applies validation and comparison at selective stages of the payment process rather than continuously. By applying matching checks only when necessary (e.g., during initial routing determination rather than at every transaction step), the system maintains high success rates while limiting time consumption.
3Measurement precision
If data cleansing is performed on dropped payment files, then payment accuracy is improved, but system resource consumption increases
Solution Approach 1:
The data cleansing process focuses only on the specific fields and records that are most critical for payment accuracy, rather than comprehensively processing all payment data. By applying cleansing operations selectively to high-impact areas (e.g., biller identification fields, account numbers), the system improves accuracy while minimizing overall resource consumption.
Solution Approach 2:
The system employs automated self-correcting algorithms that can independently identify and fix common data formatting issues without requiring extensive manual intervention or complex processing resources. The cleansing logic uses self-contained validation rules that efficiently correct errors with minimal computational overhead.
Data Source
AI summary
A dropped payment file is obtained at a computing device of an operator of an electronic funds transfer bill payment system from a client of such system. The dropped payment file includes data associated with at least one unsuccessful attempt to match payment data to a corresponding biller. The data in the dropped payment file is cleaned to create an updated dropped payment file. Matching logic is applied to the updated dropped payment file to identify at least one recommended biller to which the payment data should likely have been routed to. The client is advised of the at least one recommended biller.