Financial OTP Detection Using Message Classification Alerts
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Solution Overview
Problem
Users are often deceived into sharing one-time passwords (OTPs) for fraudulent financial transactions due to non-standard and non-uniform content of OTP messages, exacerbating monetary losses and lack of knowledge about protecting OTPs.
Innovation Solution
Utilizing artificial intelligence and machine learning to classify messages as financial or non-financial transactions, providing warnings through a trained machine-learning model that highlights or flags OTPs associated with financial transactions on portable devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If OTP messages use non-standard and non-uniform content to accommodate different transaction types and providers, then versatility and adaptability are improved, but message recognizability and user ability to identify legitimate OTPs deteriorate
Solution Approach 1:
A machine-learning-based classification system acts as an intermediary between the diverse OTP messages and the user. The system receives OTP messages with varying content formats, processes them through trained models that recognize patterns across different providers and transaction types, and presents standardized warnings or alerts to users. This mediator translates the complexity of non-uniform message content into consistent user guidance, resolving the contradiction between message versatility and recognizability.
Solution Approach 2:
The system changes the parameter of message presentation by transforming diverse OTP formats into a standardized warning interface. Instead of requiring users to interpret varying message content formats, the system modifies the output parameter to always present clear, consistent warnings when OTPs are detected, regardless of the original message's provider-specific formatting or content variations.
2Reliability
If users are provided with detailed information about OTP messages, then user awareness and caution are improved, but message complexity and processing requirements worsen
Solution Approach 1:
The system extracts only the critical information needed for user awareness - the detection of OTP messages and associated warnings - while filtering out unnecessary detailed analysis. The machine-learning models process the full message content to identify OTP patterns, but the user interface presents only the essential warning information rather than comprehensive message breakdowns, reducing perceived complexity while maintaining user caution.
Solution Approach 2:
The machine-learning classification system performs automated analysis of OTP messages without requiring user intervention in the complex processing. The system self-services by automatically detecting, classifying, and presenting warnings based on pre-trained models, eliminating the need for users to manually analyze message complexity while maintaining high reliability in OTP identification.
Data Source
AI summary
Methods and apparatus for one-time password detection and alert are disclosed. A disclosed example apparatus includes machine readable instructions, and at least one processor circuit to be programmed by the machine readable instructions to classify, with a trained machine-learning model, messages of a messaging platform of a computing device as one of a financial transaction or a non-financial transaction, and provide a warning based on a one-time password (OTP) message of the messages being classified as a financial transaction.