Real-Time Machine Learning Validation of Transfer Inputs
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Solution Overview
Problem
Data transfers often fail due to incorrect recipient information, particularly in cases where the information is long and highly specific, leading to multiple unsuccessful attempts.
Innovation Solution
Implementing a system that uses trained machine learning models, including generative artificial intelligence, to monitor and validate data transfer inputs in real-time, identifying format compliance and providing feedback to correct errors before the transfer is executed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If recipient information is made long and highly specific to ensure accurate data transfers, then transfer reliability is improved, but user input accuracy deteriorates due to the unintuitive nature of the information
Solution Approach 1:
The system provides real-time feedback to users as they input recipient information. The machine learning model analyzes the input and immediately returns validation results, guiding users to correct errors before submission. This continuous feedback loop improves user input accuracy without changing the required information format.
Solution Approach 2:
The system performs preliminary validation of recipient information before the data transfer is executed. The machine learning model checks the input against learned patterns and requirements, identifying potential errors in advance. This preliminary action prevents failed transfers and ensures reliability before the actual transfer occurs.
2Reliability
If multiple attempts are made to correct incorrect recipient information, then transfer reliability is improved, but time consumption increases
Solution Approach 1:
The machine learning model performs preliminary validation checks before the user submits the transfer request. By identifying and alerting errors in advance, the system prevents failed transfer attempts, ensuring reliability on the first try and eliminating time loss from multiple attempts.
Solution Approach 2:
Real-time feedback is provided to users during input, immediately indicating when information is incorrect or incomplete. This allows users to correct errors before submission, reducing the need for multiple attempts and minimizing time consumption while maintaining high transfer reliability.
3Reliability
If machine learning models are used to validate transfer inputs in real-time, then transfer reliability is improved, but computational resource usage increases
Solution Approach 1:
The system applies partial validation by focusing the machine learning model on specific critical aspects of the recipient information rather than analyzing every character. This selective approach maintains high validation accuracy while reducing unnecessary computational overhead and energy consumption.
Data Source
AI summary
The present disclosure relates to systems and methods for pre-checking transfer information for data transfers using trained machined learning models. There is provided a computer system, comprising a processor; a communications module coupled to the processor; and a memory coupled to the processor. The memory stores instructions that, when executed, configure the processor to monitor input of transfer input for a data transfer in an input field of an interface displayed on a device in real-time, determine a format protocol that applies to the input field, determine whether the transfer input complies with the format protocol using a trained machine learning model, generate and transmit a signal to the device receiving the transfer input in real time, the signal indicating whether the transfer input complies with the format protocol, and receive modification to the transfer input in the input field prior to execution of the data transfer.