Future Account Status Projection for Transaction Risk Scoring
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Solution Overview
Problem
Transactions that take time to process are vulnerable to risks due to potential changes in account status, such as insufficient funds or fraudulent activities, particularly evident in ACH transactions, which have a predetermined settlement period, making them susceptible to fraudulent attacks.
Innovation Solution
A machine learning algorithm is utilized to analyze account data and predict the likelihood of maintaining necessary conditions for transaction completion by generating a risk score based on historical transaction patterns and account balances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If transaction processing time is extended to ensure proper evaluation, then transaction reliability improves, but account status may change during processing creating risk
Solution Approach 1:
The system performs preliminary evaluation of the account status at the time of transaction initiation before the actual transaction processing begins. This preliminary check ensures that the account meets necessary conditions (sufficient funds, good standing) before committing to the transaction, thereby maintaining reliability without requiring extended processing time during the actual transaction execution.
Solution Approach 2:
The patent replaces traditional real-time account status verification during transaction processing with a machine learning model that predicts future account status. This substitution allows the system to assess transaction reliability based on projected account conditions rather than continuously monitoring during processing, reducing processing time while maintaining or improving reliability.
2Reliability
If machine learning prediction is used to assess future account status, then transaction risk reduction improves, but system complexity increases
Solution Approach 1:
The patent introduces a machine learning model as an intermediary component between the transaction processing system and the account management system. This model acts as a mediator that predicts future account status based on historical data, providing risk assessment without requiring direct integration with account systems during transaction processing. The intermediary nature of the ML model manages complexity by encapsulating the prediction logic in a separate, reusable component.
Solution Approach 2:
The system creates a predictive copy or projection of future account status using the machine learning model, rather than directly accessing and evaluating the actual account status during processing. This copying approach allows risk assessment based on simulated future conditions without requiring real-time access to account systems, reducing system complexity while improving risk reduction capability.
Data Source
AI summary
The present technology includes receiving, by an evaluation service, a request from a web service regarding a subject entity performing a current transaction, where the current transaction is dependent on a parameter of a user account associated with the subject entity meeting a criterion, receiving, by the evaluation service, historical account data from the user account associated with the subject entity, predicting, by the evaluation service, a projected parameter at a future time of the user account associated with the subject entity, where the evaluation service includes a trained machine learning model configured to receive the historical account data for the user account and to predict the projected parameter, and generating, by the evaluation service, a probability that the projected parameter at the future time is correct.