NAV Verification System Using Historical Variance Thresholds
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Financial institutions face challenges in verifying the accuracy of new financial data, particularly net asset values, which can lead to risks if not properly validated, necessitating a robust system to ensure data reliability.
Innovation Solution
A system that compares new net asset values to historical values, allowing data to be stored only if within a predetermined variance threshold; if the value exceeds this threshold, the system provides possible explanations for the variation, requiring user selection before data is added to the database, and allows for management of variance thresholds and explanations based on market trends.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If new NAV data is automatically stored without verification, then data processing speed is improved, but data reliability deteriorates
Solution Approach 1:
The system performs preliminary verification actions by comparing incoming NAV data against historical values and market trends before storing the data. This advance validation ensures data reliability is maintained while allowing automatic processing to proceed for normal cases, thus resolving the contradiction between processing speed and data reliability.
2Reliability
If strict verification with user confirmation is required for all NAV data, then data reliability is improved, but processing time increases
Solution Approach 1:
The system applies partial verification by requiring user confirmation only when NAV data falls outside expected ranges or trends. For data within normal parameters, automatic processing occurs without user intervention. This selective approach maintains high data reliability while minimizing processing time loss.
3Measurement precision
If variance threshold is set low for strict validation, then data precision is improved, but system adaptability deteriorates
Solution Approach 1:
The system dynamically adjusts variance thresholds based on market conditions and historical patterns. During volatile periods, thresholds are widened to accommodate normal market fluctuations, while during stable periods, tighter thresholds maintain precision. This dynamic adaptation resolves the contradiction between data precision and system adaptability.
Data Source
AI summary
Disclosed herein is a system for verifying financial data, such as a Net Asset Value (“NAV”), received from a third party. A new NAV for a fund may be supplied to the system, along with other data, for storage in a database of financial data. Upon receiving the new NAV, the system may analyze the NAV by comparing it to historical NAVs for that fund. If the NAV is within a certain variance, as compared to historical values, then the NAV may be stored as a current NAV for the fund. However, if the NAV differs from one or more historical values by more than a predetermined tolerance, the system may present to a provider of the NAV data one or more possible explanations for the variation, from among which the provider must select an explanation before the NAV is added to the database of financial data.