Financial Data Validation System Using Normalized Format and Modular Checks
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Solution Overview
Problem
Current systems for financial services data lack a mechanism to validate the accuracy and meaningfulness of the data values, leading to potential errors in decision-making processes, as they only check for format correctness and not the validity of the data values.
Innovation Solution
A computerized method and system that translates financial services data into a normalized format, using three validation modules (security, threshold, and source validation) to ensure data accuracy, with an alert system to notify of any validation failures, and dynamic threshold adjustments based on external data sources and machine learning algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current systems only check data format correctness, then the system complexity remains low, but the data reliability is insufficient because value errors cannot be detected
Solution Approach 1:
The validation system is segmented into three distinct modules: security validation module (checks expected values and criteria), threshold validation module (monitors abnormal changes using statistical thresholds), and source validation module (verifies data against external sources). This segmentation allows comprehensive validation while maintaining manageable system complexity through modular design.
Solution Approach 2:
The system performs preliminary validation actions on incoming financial data before the data is processed by downstream systems. By validating data format, security criteria, threshold compliance, and source authenticity in advance, the system prevents erroneous data from propagating through the financial processing pipeline.
2Reliability
If multiple validation modules are implemented, then the data quality improves, but the processing time increases
Solution Approach 1:
The validation system applies partial validation actions based on data characteristics and risk levels. Not all data requires the full sequence of three validation modules - the system can apply security validation alone for routine data, or add threshold and source validation for high-risk or abnormal data scenarios, optimizing the balance between validation thoroughness and processing efficiency.
3Measurement precision
If dynamic threshold adjustments are implemented, then the detection accuracy improves, but the computational resources required increase
Solution Approach 1:
The threshold validation module updates statistical thresholds periodically based on accumulated historical data patterns, rather than continuously recalculating for each data point. This periodic updating mechanism maintains high detection accuracy for abnormal changes while limiting computational resource consumption to discrete update intervals.
Data Source
AI summary
Methods and systems for providing validating financial services data is provided. The financial services data is translated into a normalized format. The normalized financial services data is validated with one or more validations modules, and an alert is transmitted if any of the validations fail.