Dynamic Fraud Detection Algorithm Using Machine Learning Feedback
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Solution Overview
Problem
Conventional systems for detecting fraudulent transactions are often ineffective, leading to completed fraudulent transactions and unnecessary card disabling due to oversensitivity or undersensitivity, failing to learn from false positives, and not being able to stop fraudulent activities in real-time.
Innovation Solution
A machine learning-based dynamic classification algorithm is applied to incoming transactions to determine fraudulence, allowing user verification and feedback, which can stop fraudulent transactions before completion, and is updated using customer and merchant data to improve accuracy and sensitivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional fraud detection systems are made more sensitive to detect fraudulent transactions, then fraudulent activity detection improves, but false positives increase causing unnecessary card disabling
Solution Approach 1:
The patent implements a dynamic fraud detection system that adjusts sensitivity thresholds based on learned patterns from user feedback and transaction data. The system transitions from static rule-based detection to adaptive machine learning models that continuously refine their classification criteria, allowing sensitivity to be optimized without proportionally increasing false positives.
Solution Approach 2:
The system incorporates feedback loops where user responses to fraud verification requests and actual fraud outcomes are fed back into the machine learning models. This feedback mechanism allows the system to learn from both false positives and false negatives, continuously improving the balance between detection sensitivity and reliability.
2Reliability
If conventional fraud detection systems are made less sensitive to reduce false positives, then card disabling reliability improves, but fraudulent transactions are not detected
Solution Approach 1:
The system performs preliminary fraud assessment using machine learning models before triggering card disabling actions. By pre-classifying transactions using learned patterns from historical data and user feedback, the system can confidently disable cards only when fraud is highly probable, improving reliability while maintaining sensitivity through the preliminary analysis stage.
Solution Approach 2:
The patent replaces mechanical rule-based detection systems with machine learning-based intelligent detection. This substitution enables the system to handle complex, non-linear patterns in fraud detection, achieving both high sensitivity and reliability by learning from data rather than following rigid predetermined rules.
3Measurement precision
If fraud detection is performed after transaction completion, then detection thoroughness improves, but transaction loss occurs before detection
Solution Approach 1:
The system performs fraud detection preliminarily during the transaction authorization phase rather than after completion. Machine learning models analyze transaction patterns in real-time and can flag suspicious transactions for additional verification or immediate rejection, preventing fraudulent transactions from completing while maintaining accurate detection through pre-assessment.
Solution Approach 2:
The patent implements expedited fraud detection pathways where high-risk transactions identified by the machine learning system are fast-tracked through additional verification steps. This allows the system to rush through necessary security checks for suspicious transactions without delaying legitimate transactions, reducing time loss for fraud prevention while maintaining detection accuracy.
4Measurement precision
If machine learning algorithms are continuously updated with user feedback, then detection accuracy improves, but system complexity increases
Solution Approach 1:
The system implements self-service mechanisms where the machine learning models automatically retrain and update themselves using collected user feedback and transaction data. This self-updating capability reduces the need for manual system configuration and updates, managing complexity through automation while continuously improving detection accuracy through learned patterns.
Solution Approach 2:
The patent creates a universal feedback processing framework that handles multiple types of input data (user responses, transaction outcomes, behavioral patterns) through a single machine learning pipeline. This multi-functional approach consolidates complexity into a unified system that can process diverse data types and update models comprehensively, rather than requiring separate systems for each data source.
Data Source
AI summary
Disclosed are systems and methods for mitigation of fraudulent transactions. In some embodiments, a server is communicatively coupled to a user device, and is configured to receive a proposed transaction from a merchant device communicatively coupled to the server, apply a dynamic classification algorithm to the proposed transaction to determine if the proposed transactions appears to be fraudulent, generate a user verification request when the proposed transaction appears to be fraudulent, transmit the user verification request to a user computing device communicatively coupled to the server, receive an approval or a refusal of the proposed transaction based on the user verification request, and process the proposed transaction based on the received approval or refusal of the proposed transaction.