Real-Time Scam Detection With ML Scoring and User Alerts
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Solution Overview
Problem
Financial institutions and technology companies lack effective real-time fraud detection mechanisms to identify fraudulent activities before transaction execution, leading to increased risks of irreversible payments and scams.
Innovation Solution
Implementing machine learning techniques, including a first ML model for scoring potential fraud and a second LLM for user education, to generate alerts and provide user-selectable options before completing transactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time fraud detection using machine learning is implemented, then the ability to identify fraudulent activity before transaction execution is improved, but the device complexity increases
Solution Approach 1:
The fraud detection system is segmented into multiple independent components: a first machine learning model for fraud scoring, a second machine learning model (LLM) for user education, and a notification system. Each component handles a specific function, allowing the system to achieve high reliability through specialized processing while managing complexity through modular architecture.
Solution Approach 2:
The system performs preliminary fraud detection and scoring before the transaction execution is completed. The first ML model evaluates transaction parameters in advance and generates a fraud score, enabling proactive identification of fraudulent activity. This preliminary action allows the system to prevent fraud before it occurs rather than reacting after damage is done.
2Measurement precision
If machine learning models are used for fraud detection, then the accuracy of fraudulent activity identification is improved, but the processing time increases
Solution Approach 1:
The detection process is segmented into sequential stages: first the ML model performs rapid scoring based on transaction parameters, then only if needed does the LLM generate educational content. This segmentation allows the system to achieve high accuracy through comprehensive analysis while minimizing processing time by avoiding unnecessary steps for low-risk transactions.
Solution Approach 2:
The system applies partial action by using the first ML model for initial screening of all transactions, then applying the more resource-intensive LLM only to high-risk cases that require user education. This approach balances processing speed with detection accuracy by not over-engineering the entire process at maximum capacity.
3Loss of information
If user education options are provided before transactions, then user awareness of scams is improved, but the ease of operation decreases
Solution Approach 1:
The system provides feedback to users in the form of educational information about potential scams before they complete a transaction. The LLM generates tailored educational content based on the detected fraud indicators, helping users understand why the transaction may be fraudulent. This feedback mechanism improves user awareness while maintaining ease of operation by presenting information in an accessible format.
Solution Approach 2:
User education is provided as a preliminary action before the transaction is completed. The system presents educational information and user-selectable options (proceed with transaction or cancel) in advance, allowing users to make informed decisions without complicating the overall transaction flow. The educational content is delivered through a user interface that integrates smoothly with the existing transaction process.
Data Source
AI summary
A method and a system for performing real-time fraud detection by proactively identifying fraudulent activity before executing a transaction are provided. The method includes: receiving, from a user, first information that relates to a proposed transaction; providing the first information as an input to a machine learning model; using the machine learning model to generate an output that relates to potentially fraudulent activity associated with the first information; generating, based on the first output, an alert message that includes second information that relates to notifying the user about the potentially fraudulent activity; and transmitting the alert message to the user. The output may include a score that relates to a likelihood that the first information is associated with actual fraudulent activity, and the score may be normalized to fall in a range of between zero and ten.