Sleep Pattern Analyzer for Fraud Detection
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Solution Overview
Problem
Current systems face challenges in determining fraudulent transactions, particularly when cardholders are asleep, as existing fraud detection methods lack the ability to accurately identify transactions initiated during sleep times, leading to potential false declines or approvals.
Innovation Solution
A network-based sleep pattern analyzer system that collects and analyzes sleep data from user devices to determine when a cardholder is likely asleep, flagging transactions initiated during these times as potentially fraudulent, and transmitting notifications to relevant parties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional fraud scoring systems are used to detect fraudulent transactions, then transaction security is improved, but false declines occur when legitimate transactions are flagged during cardholder sleep times
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing sleep pattern data before transaction evaluation occurs. Sleep patterns are established in advance through continuous monitoring, allowing the system to determine whether a cardholder is likely asleep at the time of a transaction attempt, thereby preventing false declines of legitimate transactions
Solution Approach 2:
The system introduces sleep pattern data as an intermediary factor in the fraud detection process. This additional data layer mediates between traditional fraud scoring and final transaction approval, providing context about cardholder availability that helps distinguish legitimate transactions from fraudulent ones
2Ease of operation
If transactions are approved without sleep pattern analysis, then user convenience is maintained, but fraudulent transactions during sleep times may be approved
Solution Approach 1:
The system employs self-service by using automatically collected sleep pattern data from the cardholder's own device without requiring manual input or verification. The sleep tracking occurs passively in the background, and the system autonomously uses this data to make fraud determination decisions, maintaining convenience while improving security
3Reliability
If sleep data is collected and analyzed centrally, then fraud detection capability is improved, but system complexity increases
Solution Approach 1:
The system merges sleep tracking functionality with the existing payment application, combining two separate functions into a single integrated system. The sleep pattern analyzer is incorporated into the payment processing infrastructure, allowing fraud detection and sleep monitoring to work together through a unified platform
Data Source
AI summary
A sleep pattern analyzer (SPA) system for capturing and analyzing sleep data and sleep pattern data is provided. The SPA system is configured to receive sleep data associated with a user, the sleep data including a registered user identifier and at least one sleep time stamp, and store the sleep data in a sleep pattern database. The SPA system is also configured to receive transaction data for a transaction initiated by a consumer with a merchant. The SPA system is further configured to match the consumer identifier to the registered user identifier, generate a fraud notification message when the transaction time stamp overlaps with the at least one sleep time stamp, and transmit the fraud notification message to at least one of an issuer, the merchant, and the consumer associated with the consumer identifier.