Automated Transaction Management System for Reducing Processing Delays
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Solution Overview
Problem
Delays in electronic transactions due to transaction holds result in increased processing times and consumer inconvenience, necessitating an improved transaction management system that can predict and automate responses to address these delays.
Innovation Solution
A system that captures user information, including facial, gesture, audio, and other data, to predict user interaction indicators, allowing service providers to automate transaction management actions and reduce customer service contacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If transaction holds are implemented for risk management, then transaction security is improved, but transaction processing time increases and customer satisfaction deteriorates
Solution Approach 1:
The system performs preliminary analysis of user information, device data, and transaction patterns before the transaction is completed. Risk assessments and fraud detections are conducted in advance during the transaction initiation phase, allowing holds to be applied only when necessary rather than as a default procedure, thus reducing unnecessary delays while maintaining security.
Solution Approach 2:
The patent replaces manual customer service intervention with an automated machine learning system that analyzes user data, device information, and transaction patterns. This automated system makes real-time decisions about transaction holds, eliminating the need for customers to contact support and significantly reducing processing time while maintaining security standards.
2Ease of operation
If manual customer service intervention is used to address transaction delays, then customer service quality is improved, but operational costs increase and processing efficiency decreases
Solution Approach 1:
The system enables self-service by automatically resolving transaction delays through machine learning-based analysis and automated decision-making. The system independently assesses risk, detects fraud, and makes hold decisions without requiring customer service agent intervention, thereby maintaining service quality while dramatically improving processing efficiency and reducing operational costs.
Solution Approach 2:
The system implements continuous feedback loops where transaction outcomes, customer behaviors, and fraud patterns are analyzed to continuously improve the machine learning models. This feedback mechanism ensures that the automated system maintains high service quality by adapting to new patterns and edge cases without requiring manual intervention.
3Measurement precision
If comprehensive user data collection is implemented, then prediction accuracy is improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system segments user data collection into distinct categories: device information, user behavior patterns, transaction history, and biometric data. Each segment is processed by specialized machine learning models trained for specific purposes. This segmentation allows the system to maintain high prediction accuracy while managing complexity through modular architecture, where each component handles a specific data type independently.
Data Source
AI summary
Systems and methods for transaction management include receiving, from a user device associated with a user, a transaction request and receiving, through a network in a token form, user information associated with the user while the user is accessing transaction information from the user device. The user information comprises data associated with an action by user or a biometric of the user captured by the user device. Based on data associated with the transaction request, a delay in processing the transaction request due to a transaction rule is determined, and then based on the user information, a user interaction indicator associated with the transaction is determined. A transaction management action associated with the transaction is performed based on the user interaction indicator.