Transaction Card Movement Sensing for Fraud Detection
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Solution Overview
Problem
Financial institutions struggle to accurately identify and prevent fraudulent transactions using transaction cards, leading to wastage of computing and network resources due to improper fraud detection, and users face similar resource consumption in correcting false fraud alerts.
Innovation Solution
A fraud detection model trained with historical transaction, biometric, and card movement data is used to determine a fraud score, enabling informed decision-making on transactions by analyzing user nervousness and card shaking patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional fraud detection measures are used, then fraud detection capability is provided, but computing and network resources are wasted due to improper fraud detection and false alerts
Solution Approach 1:
The patent replaces traditional mechanical fraud detection methods with biometric sensing technology. The transaction card incorporates sensors (accelerometers, gyroscopes, biometric sensors) that detect physical characteristics and card movement patterns, substituting conventional verification mechanisms with advanced biometric authentication to improve detection accuracy while reducing false positives
Solution Approach 2:
The patent changes the detection parameters from traditional transaction data alone to multiple biometric parameters including card movement characteristics, sensor data, and user physiological responses. By analyzing these varied parameters through machine learning models, the system achieves more accurate fraud identification, thereby reducing resource waste from false alerts
2Reliability
If traditional fraud detection measures are used, then fraud detection capability is provided, but users face resource consumption in correcting false fraud alerts
Solution Approach 1:
The patent replaces manual fraud verification processes with automated biometric authentication. The transaction card's sensors and processing capabilities enable automatic detection and verification, eliminating the need for users to manually correct false alerts and saving their time
Solution Approach 2:
The transaction card performs self-verification through its integrated biometric sensors and processing unit. The card automatically detects fraud conditions and communicates verification status without requiring user intervention, making the fraud detection process self-serving and time-efficient
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances fraud detection accuracy, conserving computing and network resources by reducing false alerts and reimbursements, and improving transaction security.
Implementation Method 1
receiving, by the device and from an accelerometer of the transaction card, card movement data relating to the measure of shaking of the transaction card by the user during the transaction
Data Source
AI summary
A fraud detection platform may receive transaction data relating to a transaction conducted by a user with a transaction card. The fraud detection platform may receive, from a biometric sensor of the transaction card, biometric data relating to one or more biometric characteristics of the user during the transaction. The fraud detection platform may receive, from an accelerometer of the transaction card, card movement data relating to a measure of shaking of the transaction card by the user during the transaction. The fraud detection platform may process the transaction data, the biometric data, and the card movement data, with a fraud detection model, to determine a fraud score associated with the transaction. The fraud detection platform may perform one or more actions based on the fraud score.