Feature Engineering for Fraud Crafting the Signals that AI can Understand Part 1

In the relentless fight against credit and debit card fraud, even the simplest pieces of transaction data hold immense power for Artificial Intelligence (AI). My latest post dives into the fundamental transaction attributes that form the bedrock of AI-driven fraud prevention.

We begin by exploring the core transaction DNA:

• 𝗧𝗿𝗮𝗻𝘀𝗮𝗰𝘁𝗶𝗼𝗻 𝗔𝗺𝗼𝘂𝗻𝘁: Understanding the monetary value exchanged.
• 𝗧𝗿𝗮𝗻𝘀𝗮𝗰𝘁𝗶𝗼𝗻 𝗧𝗶𝗺𝗲: Analyzing the “when” of the exchange for anomalies.
• 𝗟𝗼𝗰𝗮𝘁𝗶𝗼𝗻 𝗗𝗮𝘁𝗮: Pinpointing the “where” to identify suspicious activity.
• 𝗠𝗲𝗿𝗰𝗵𝗮𝗻𝘁 𝗖𝗮𝘁𝗲𝗴𝗼𝗿𝘆 𝗖𝗼𝗱𝗲 (𝗠𝗖𝗖): Categorizing the business to add context.
• 𝗖𝗮𝗿𝗱 𝗜𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻: Examining the payment instrument details.

But the story doesn’t end there. We further unravel how channel-specific indicators add crucial layers of context, enabling AI to accurately distinguish between legitimate and fraudulent behavior across different transaction environments:

• 𝗖𝗮𝗿𝗱-𝗣𝗿𝗲𝘀𝗲𝗻𝘁 (𝗖𝗣) 𝗩𝗮𝗿𝗶𝗮𝗯𝗹𝗲𝘀: Examining data from physical interactions.
• 𝗖𝗮𝗿𝗱-𝗡𝗼𝘁-𝗣𝗿𝗲𝘀𝗲𝗻𝘁 (𝗖𝗡𝗣 – 𝗘-𝗰𝗼𝗺𝗺𝗲𝗿𝗰𝗲/𝗠𝗼𝗯𝗶𝗹𝗲) 𝗩𝗮𝗿𝗶𝗮𝗯𝗹𝗲𝘀: Understanding the digital footprint of online and mobile transactions.
• 𝗖𝗮𝗿𝗱-𝗡𝗼𝘁-𝗣𝗿𝗲𝘀𝗲𝗻𝘁 (𝗖𝗡𝗣 – 𝗠𝗢𝗧𝗢) 𝗩𝗮𝗿𝗶𝗮𝗯𝗹𝗲𝘀: Analyzing remote interaction data.

This post is your essential guide to understanding the seemingly basic yet profoundly important data elements that empower AI to navigate the complexities of fraud detection. Swipe through to decode the essential data science behind a robust algorithmic shield!