Part 03 - Unlocking Effective Credit Card Fraud Prevention: Key Business Rules and Data-Driven Strategies

Introduction

Credit card fraud poses a major threat. Effective mitigation demands strategic rules, data-driven insights, multi-factor authentication, and real-time monitoring. Machine learning and transaction pattern analysis enhance detection, while historical data anticipates emerging threats. This article outlines key strategies for extracting actionable insights, bolstering security, and maintaining trust in payment systems.

This article delves into essential fraud triggers and business rules derived from historical data analysis, highlighting key areas such as "Multiple Transactions Across Distant Locations in a Short Time," "Transactions at Unusual Hours," "Transactions Near the Card’s Credit Limit," and "Duplicate Transactions."

Fraud Detection: Strategic Business Rules and Data-Driven Insights Derived from Historical Data

?? Multiple Transactions Across Distant Locations in a Short Time

Flag transactions that occur in geographically distant locations within a short timeframe.

? Business Explanation: It's improbable for a cardholder to physically be in two distant locations in a very short time.

?? Industry Example & Sample Data: A cardholder with Customer ID CCA#123456 made a transaction of $1,254 in New York at 2:00 PM, followed by a transaction of $1,583 in Tokyo at 4:00 PM.

?? Data Derivation: Compare the time and location of consecutive transactions to flag impossible travel scenarios.

?? Transactions at Unusual Hours

Flag transactions made at unusual hours for the cardholder.

? Business Explanation: Fraudsters often operate at night when the cardholder is less likely to notice.

?? Industry Example & Sample Data: A cardholder (Customer ID CCA#123456) who usually makes daytime transactions suddenly processed a $1,610 transaction (Transaction ID TR#12345) at 3 AM.

?? Data Derivation: Analyze transaction times and flag those that occur during unusual hours for the cardholder.

?? Transactions Just Below the Card’s Credit Limit

Flag transactions that are just below the card’s credit limit.

? Business Explanation: Fraudsters may attempt to maximize the use of the available credit without exceeding the limit, which might trigger alerts.

?? Industry Example & Sample Data: A cardholder with Customer ID CCA#123456, who has a $10,000 credit limit, made a series of transactions totaling $9,955 on August 1, 2024.

?? Data Derivation: Compare transaction amounts to the credit limit and flag those just below the limit.

?? Duplicate Transactions

Flag duplicate transactions at the same merchant for the same amount within a short time.

? Business Explanation: Fraudsters may accidentally or intentionally process multiple identical transactions.

?? Industry Example & Sample Data: A card was charged twice for $107 at the same gas station (Merchant ID MCA#12345) with timestamps of 10:00 AM and 10:05 AM.

?? Data Derivation: Identify identical transactions within a short timeframe and flag them as potential duplicates.

Conclusion

Combatting credit card fraud requires a proactive strategy, combining data-driven insights with real-time monitoring and machine learning to detect patterns and predict threats. Credit card companies must deploy a robust, continuously updated monitoring system to address evolving risks, safeguarding assets and maintaining consumer trust.

Important Note

This newsletter article is designed to educate a broad audience, encompassing professionals, faculty, and students from both engineering and non-engineering disciplines, regardless of their level of computer expertise.