Automated Data Verification via Risk-Based Segmentation
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Solution Overview
Problem
The conventional manual data validation process in financial institutions is inefficient and unable to promptly identify and correct high-risk data anomalies, leading to potential fraudulent transactions and downstream issues due to the large volume of data and the inability to postpone transaction processing.
Innovation Solution
A system that ranks datasets by risk levels, applies rules to identify anomalous data elements, and groups them for further analysis, using a ranking engine and evaluation circuitry to automate the process, thereby reducing human error and increasing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual data validation processes are used, then data verification can be performed, but the process is inefficient and cannot promptly identify high-risk data anomalies due to large data volume
Solution Approach 1:
The patent segments data validation into risk-based categories, prioritizing high-risk data elements for automated validation while applying different validation strategies to different data segments. This allows efficient processing of large volumes of data by focusing automated resources on the most critical segments.
Solution Approach 2:
The patent replaces manual mechanical validation processes with automated computer-based validation systems that use algorithms and rules to detect anomalies. This substitution enables rapid processing of large data volumes while maintaining or improving verification accuracy through consistent automated application of validation rules.
2Measurement precision
If exhaustive error-detection processes are applied, then data accuracy can be improved, but transaction processing time increases and real-time use of recent information is prevented
Solution Approach 1:
The patent implements preliminary validation actions by validating data at the point of entry or transmission, before it enters downstream systems. This preliminary action catches errors early, allowing for rapid correction without delaying subsequent processing. The system performs risk-based validation checks in advance to prevent erroneous data from propagating through the system.
Solution Approach 2:
The patent applies partial validation action by focusing validation resources on high-risk data elements rather than uniformly validating all data exhaustively. This selective approach maintains data accuracy for critical fields while reducing overall processing time by applying lighter validation to lower-risk data.
3Speed
If real-time or near-real-time data transmission is used, then recent information can be used effectively for fraud detection, but the risk of erroneous data entering databases increases
Solution Approach 1:
The patent performs preliminary validation checks on data at the time of transmission or entry, before it is stored in databases or shared with third parties. This preliminary action filters out erroneous data in real-time, allowing fast transmission of verified data while preventing bad data from entering downstream systems.
Solution Approach 2:
The patent implements feedback mechanisms that monitor data quality and provide real-time or near-real-time feedback on validation status. This feedback allows the system to quickly identify and correct erroneous data entries, maintaining data integrity even during rapid transmission cycles. Validation results feed back into the process to guide corrective actions.
Data Source
AI summary
Systems, apparatuses, methods, and computer program products are disclosed for verifying record data. An example method includes identifying, by evaluation circuitry, anomalous data elements from data elements of a record included in a dataset. The example method also includes determining, by the evaluation circuitry, a root-cause analysis operation resolution time frame guideline based on an anomaly rate of the anomalous data elements in relation to an anomaly rate threshold value. The example method also includes generating, by the evaluation circuitry, a recommendation to correct a cause of the anomalous data elements based on the root-cause analysis operation resolution time frame guideline.