Virtual Token Authentication for Financial Security
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Solution Overview
Problem
Online activities are vulnerable to fraudulent access due to anonymity and lack of physical presence, particularly during online transactions where private financial information is exposed.
Innovation Solution
A computer-based method and system that dynamically maps a virtual account number to an actual financial account, using a machine learning model to predict and authorize modifications, thereby reducing the need for manual input and enhancing security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If virtual tokens are used to protect financial information during online transactions, then security against fraudulent access is improved, but system complexity increases due to the need for dynamic mapping and machine learning models
Solution Approach 1:
The patent introduces virtual tokens as intermediary elements that mediate between users and their actual financial accounts. These tokens dynamically map to real account numbers through a server system, allowing secure transactions without exposing actual financial information. The machine learning model acts as an additional intermediary layer that predicts authorization outcomes, further enhancing security while managing the complexity through automated decision-making.
Solution Approach 2:
The system dynamically changes the parameter of account identification by replacing static account numbers with dynamic virtual tokens. These tokens can be regenerated for different transactions or contexts, transforming the security model from static credential verification to dynamic token-based authentication. The machine learning model adds another layer of parameter change by predicting authorization based on historical data patterns.
2Reliability
If manual input is required for transaction authorization, then security control is maintained, but processing time and user convenience deteriorate
Solution Approach 1:
The system performs preliminary actions by pre-generating virtual tokens and storing user authorization preferences before actual transactions occur. The machine learning model is trained in advance on historical transaction data to predict authorization outcomes. During live transactions, the system can quickly retrieve pre-generated tokens and apply the trained model, significantly reducing processing time while maintaining security through pre-established controls.
Solution Approach 2:
The machine learning model enables self-service authorization by automatically predicting transaction approval or rejection based on learned patterns from historical data. The system serves itself by making authorization decisions without requiring manual human intervention for each transaction, while still maintaining security through the predictive analytics framework. Users can review and override automated decisions when needed.
Data Source
AI summary
Systems and methods of the present disclosure enable virtual tokens for dynamically mapping virtual account numbers to actual accounts. The systems and methods include receiving an electronic data modification instruction of a virtual record modification to cause at least one data modification to at least one attribute of a virtual record data structure associated with the actual account. A user record associated with the virtual record is determined, and a record modification preference associated with the user record is determined based on the modification condition. An authorization instruction is generated to authorize the electronic data modification instruction to cause the modification by instructing at least one database management system to modify the user record according to the record-specific modification.