Section: Abuse, Scam & Arbitrage Detection

This section delves into the significant issues associated with identifying and preventing various abuses within gift card systems, including scams, arbitrage, and fraud. It highlights the necessity of scam detection, arbitrage prevention, fraud signal identification, exploitation via digital channels, and the reduction of false positives in fraud detection processes.

The section includes forthcoming discussions on:

• AI Detection of Gift Card Scams: This involves identifying common scam patterns, analyzing communications through NLP, blocking fraudulent accounts, understanding geographic and demographic variations, and predicting risks.
• AI Detection of Arbitrage: This topic covers the investigation of arbitrage schemes, tracking abnormal market activities, assessing cross-border pricing risks, employing predictive analytics, and distinguishing open vs. closed-loop systems.
• Effective Signals for Fraud Detection: It focuses on understanding velocity patterns, device fingerprinting, conducting social network analysis, detecting money laundering, and analyzing time-based patterns.
• Digital Channel Exploitation by Scammers: This involves exploring phishing attacks, the use of compromised accounts, dark web markets, social media spread, and CAPTCHA bypass techniques.
• Reducing False Positives in Fraud Detection: The focus here is on reducing friction, minimizing false positives, applying reinforcement learning, using explainable AI, and refining customer support processes.