Financial Data Refresh Prioritization via Transaction Probability
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Solution Overview
Problem
Current methods for refreshing financial data, such as online data pulls and batch data pulls, are inefficient and resource-intensive, leading to computing resource and bandwidth constraints, as well as network traffic issues for financial services providers and institutions.
Innovation Solution
A computer-implemented method that prioritizes financial data retrieval by using predictive models to determine the likelihood of new transactions in financial accounts, allowing for a batch data pull that focuses on accounts with the highest probability of changes, thereby reducing unnecessary data refreshes and optimizing resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If batch data pull is performed for all login identifications, then data currency is improved, but computing resource usage and network bandwidth consumption increase
Solution Approach 1:
The patent segments the population of login identifications into multiple priority groups based on account activity levels. High-priority accounts (those with recent transactions) are refreshed first, while low-priority accounts (those without recent transactions) are refreshed later or skipped. This segmentation allows the system to maintain data currency for the most important accounts while reducing overall computing resource consumption.
Solution Approach 2:
The patent applies local quality by treating different login identifications differently based on their specific characteristics. Instead of uniformly refreshing all accounts, the system identifies and prioritizes accounts with higher likelihood of transactions (local high-need areas) while reducing or eliminating refreshes for accounts with lower likelihood (local low-need areas), optimizing resource allocation to where it is most needed.
2Reliability
If batch data pull is performed for all login identifications, then data currency is improved, but network bandwidth consumption increases
Solution Approach 1:
The patent segments the population of login identifications into multiple priority groups based on account activity levels. High-priority accounts (those with recent transactions) are refreshed first, while low-priority accounts (those without recent transactions) are refreshed later or skipped. This segmentation allows the system to maintain data currency for the most important accounts while reducing overall network bandwidth consumption.
Solution Approach 2:
The patent implements partial action by refreshing only a portion of the login identifications in each batch, specifically those with the highest priority based on transaction likelihood. Rather than performing complete refreshes on all accounts, the system performs partial refreshes on the most critical accounts, achieving acceptable data currency with reduced network bandwidth usage.
3Reliability
If online data pull is performed for each user login, then data accuracy is improved, but computing resource usage and network bandwidth consumption increase
Solution Approach 1:
The patent performs preliminary action by pre-calculating priority scores for all login identifications based on their transaction history and activity patterns before the actual data pull occurs. This preliminary prioritization allows the system to efficiently determine which accounts need refreshing without performing expensive online data pulls for every single account, thereby improving processing efficiency while maintaining data accuracy for high-priority accounts.
Data Source
AI summary
Computerized systems and methods for efficiently refreshing financial data for financial accounts for login identifications of users at respective financial institutions in a batch data pull via a communication network. The login identifications are prioritized for being refreshed based on determining a probability that each login identification has a new transaction since the last successful refresh of the login identification using a learning algorithm utilizing past financial data. The login identifications are then refreshed in a batch data pull from the financial institutions in an order from highest probability to lowest probability of having a new transaction since the last batch data pull.