Deposit Data Modeling for Financial Payment Quality
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Solution Overview
Problem
Financial institutions face quality-related defects in payments that lead to customer dissatisfaction and operational inefficiencies, including undetected anomalous account treatments and unfulfilled operational service level agreements, resulting in foregone revenues and operational losses.
Innovation Solution
Systems and methods for modeling deposits' data integrity and validation, including storing deposits data, conducting production and test cycles, compiling critical interface data, and using equations to determine pre-release effects of production changes, thereby identifying and validating interest rate calculations and detecting anomalies before customer detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional payment processing systems are used without comprehensive validation, then operational speed is maintained, but quality-related defects in payments occur leading to customer dissatisfaction and operational inefficiencies
Solution Approach 1:
The system performs preliminary validation of deposits data before processing payments by conducting test production cycles and comparing critical interface data between production and test environments. This preliminary action identifies potential quality defects before they affect actual payments, resolving the contradiction by preventing defects without adding delays to the payment processing itself.
Solution Approach 2:
The system creates a copy of the production environment as a test environment with identical data structures and processing logic. By validating payments in this copied test environment first, the system ensures payment quality without impacting the speed of actual production payments, as the validation occurs in parallel or beforehand.
2Reliability
If comprehensive data validation and testing cycles are implemented, then payment quality and compliance are improved, but processing time and system complexity increase
Solution Approach 1:
The system performs compliance validation and regulatory checks in advance by running test production cycles before actual payments are processed. This preliminary compliance verification ensures that all regulatory requirements are met before payments go live, preventing rework and delays while maintaining compliance standards.
Solution Approach 2:
The system implements a feedback mechanism by comparing critical interface data from production cycles with data from test cycles. This feedback loop automatically identifies discrepancies and validates compliance, reducing the need for manual reviews and minimizing processing time while ensuring regulatory adherence.
3Reliability
If manual detection of anomalous account treatments is used, then system complexity is minimized, but customers may discover anomalies before the financial institution, leading to loss of trust and potential financial loss
Solution Approach 1:
The system uses a test environment that copies production data and processing logic to automatically detect anomalous behaviors. This automated detection in the copied test environment identifies issues before they reach customers, maintaining system reliability without requiring complex manual monitoring systems.
Solution Approach 2:
The system performs self-validation by automatically comparing its own processing results between production and test environments. This self-service approach detects anomalous behaviors without external intervention, maintaining simplicity while improving detection capabilities through automated internal monitoring.
4Productivity
If production changes are released without pre-release validation, then deployment speed is maintained, but systematic inefficiencies and quality defects may go undetected, resulting in operational losses
Solution Approach 1:
The system performs pre-release validation by conducting test production cycles with proposed changes before deploying them to production. This preliminary testing identifies quality defects and systematic inefficiencies in advance, allowing rapid deployment of validated changes without compromising production quality.
Solution Approach 2:
The system creates a test copy of the production environment to validate changes before release. By testing changes in this copied environment first, the system ensures deployment speed is maintained while quality is verified, as only validated changes are deployed to production without requiring extensive manual verification.
Data Source
AI summary
Systems and methods according to the invention preferably determine errors in a financial institutions implementation of production changes, production modifications and/or a new production release by comparing critical interface data from a production cycle to critical interface data from a test environment cycle.