Part 09 - Unlocking Effective Credit Card Fraud Prevention: Key Business Rules and Data-Driven Strategies

Introduction

Credit card fraud poses a serious risk, necessitating effective prevention strategies. Essential measures include applying specific rules, leveraging data-driven insights, employing multi-factor authentication, and maintaining real-time monitoring. Integrating machine learning with transaction pattern analysis enhances detection capabilities, and historical data helps in anticipating potential threats. This article explores fundamental methods for deriving actionable insights, improving security protocols, and fostering trust in payment systems.

This article explores critical fraud triggers and business rules identified through historical data analysis, focusing on key areas such as "Transactions with Mismatched Billing and Shipping Addresses," "Repeated Declines Followed by Approval," "Transactions on Recently Reported Lost/Stolen Cards," and "Excessive Authorization Attempts."

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

?? Transactions with Mismatched Billing and Shipping Addresses

Flag transactions where the billing and shipping addresses differ significantly.

?? Business Explanation: Fraudsters often use stolen credit cards to ship goods to a different address. Mismatched addresses can be an indicator of fraud.

?? Industry Example & Sample Data: For the transaction of $800 on August 1, 2024, the cardholder with CID#12345 has a billing address in California and a shipping address in Florida.

?? Data Derivation: Compare billing and shipping addresses and flag transactions with significant differences.

?? Repeated Declines Followed by Approval

Flag accounts that experience multiple declined transactions followed by an approved transaction.

?? Business Explanation: Fraudsters may repeatedly try to guess the correct card details until a transaction is approved.

?? Industry Example & Sample Data: Cardholder CID#12345 had three declined attempts for a transaction amount of $440, followed by a successful transaction of $440 on August 1, 2024.

?? Data Derivation: Monitor transaction approval statuses and flag accounts with multiple declines followed by an approval.

?? Transactions on Recently Reported Lost/Stolen Cards

Flag any transaction on a card reported lost or stolen.

?? Business Explanation: Transactions on a reported lost or stolen card are highly suspicious and likely fraudulent.

?? Industry Example & Sample Data: On August 1, 2024, an attempt was made to purchase $440 using a card (CID#12345) that had been reported lost the previous day, July 31, 2024.

?? Data Derivation: Match transaction data with lost/stolen card reports and flag any associated transactions.

?? Excessive Authorization Attempts

Flag accounts with excessive authorization attempts.

?? Business Explanation: Excessive authorization attempts can indicate a brute-force attack or an attempt to validate stolen card details.

?? Industry Example & Sample Data: On August 1, 2024, numerous small transactions were attempted to verify the validity of card CID#12345.

?? Data Derivation: Track the number of authorizations attempts per account and flag accounts exceeding a certain threshold.

Conclusion

A proactive strategy is crucial in the fight against credit card fraud, integrating data-driven insights, real-time monitoring, and machine learning to identify patterns and anticipate threats. Credit card companies need to deploy robust, constantly updated monitoring systems to tackle evolving risks. By harnessing these technologies, they can enhance asset protection, prevent fraud, and uphold consumer trust. Anticipating new threats strengthens defenses and bolsters security measures in the ever-changing financial landscape.

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.