Understanding Business Rules in Fraud Prevention

In the constantly evolving landscape of financial crimes, fraud prevention remains a high-priority concern for businesses and organizations worldwide. While technology and machine learning are increasingly playing an instrumental role in fraud detection, traditional methods like business rules still hold substantial value. Understanding how business rules function in fraud prevention is critical for creating a balanced, effective system for identifying and stopping fraudulent activities.

What Are Business Rules?

Business rules are explicit criteria used to define the conditions under which a specific action will take place. In the context of fraud prevention, these rules are designed to flag suspicious behavior or transactions that could indicate fraudulent activity. Business rules can be as simple as setting a limit on the number of transactions allowed in a specific timeframe or as complex as detecting patterns indicative of money laundering.

Role of Business Rules in Fraud Prevention

Real-Time Monitoring

Business rules provide a first line of defense by monitoring transactions in real-time. For instance, if a credit card transaction occurs in a foreign country just minutes after a transaction in the home country, a business rule can flag this as suspicious.

Versatility

The rules can be tailored to the unique needs and risk profile of an organization. This means that businesses can adjust their rule sets as they grow, diversify, or enter new markets.

Easy to Implement

Compared to machine learning algorithms that require a vast dataset for training, business rules are relatively easier and faster to implement. They do not require advanced expertise in data science, making them accessible for smaller organizations.

Common Business Rules With High Conviction

Multiple Failed Login Attempts

• Rule: Flag accounts that have multiple failed login attempts within a short time frame.
• Example: If an account has 5 failed login attempts within 2 minutes, flag it for potential brute-force attack.

Large Transactions

• Rule: Flag transactions that exceed a certain predefined monetary threshold.
• Example: If a customer who usually makes transactions around $100 suddenly makes a transaction of $10,000, flag it for review.

Rapid Sequence of Transactions

• Rule: Flag accounts that perform a series of transactions within a short period.
• Example: If an account makes 10 transactions within 10 minutes, each below $50, flag it for potential structured money laundering or fraud.

Geographic Anomalies

• Rule: Flag transactions that originate from geographical locations known for high rates of fraud.
• Example: If an account based in the United States suddenly makes a transaction from a location known for high fraud rates, such as a problematic IP address in a different country, flag it for review.

Transaction Velocity Checks

• Rule: Limit the number of transactions for a single account within a defined period.
• Example: No more than 5 ATM withdrawals are allowed within 24 hours.

Amount Thresholds

• Rule: Flag transactions that exceed a certain amount for further investigation.
• Example: Any single transaction exceeding $10,000 should be reviewed.

Geographical Flags

• Rule: Trigger an alert for transactions occurring in different geographic locations within a short period.
• Example: If a credit card is used in New York and Paris within a span of 2 hours, flag it as suspicious.

Impossible Travel

• Rule: Alert for transactions that happen in geographically distant places within an improbable time frame.
• Example: If an account logs in from the United States and attempts a purchase in Australia within an hour, flag the activity.

Multiple IP Addresses

• Rule: Flag accounts that are accessed from multiple IP addresses within a short period.
• Example: If an account is accessed from more than 3 different IP addresses within 24 hours, flag it.

New Device Alert

• Rule: Flag the first transaction made from a new device.
• Example: Any purchase or account login from a new device should be reviewed.

High-Risk Locations

• Rule: Flag transactions originating from or destined to high-risk countries or regions.
• Example: Any wire transfer to a high-risk country should be flagged for review.

Change in Behavior Patterns

• Rule: Identify and flag any significant deviations from a customer’s typical spending behavior.
• Example: If a customer who usually makes small purchases suddenly makes a large purchase, flag it.

Account Changes Followed by Transactions

• Rule: Flag a transaction if it immediately follows a change in account information, like email or password.
• Example: If the shipping address on an account is changed, and a large purchase is made immediately after, flag the activity.

Frequent Small Transactions

• Rule: Flag multiple small transactions that are processed in a quick succession, which may be an attempt to evade detection.
• Example: If more than 5 transactions are made within 2 minutes, each less than $5, flag them.

Account Aggregation

• Rule: Alert if a single account receives multiple payments from different sources within a short period.
• Example: If an account receives 4 different payments from different accounts within an hour, it may be an indication of money laundering.

Many-to-One or One-to-Many Transactions

• Rule: Flag when multiple accounts are directing funds to a single account, or when a single account is sending funds to multiple accounts.
• Example: If a single account receives payments from more than 5 different accounts within a day, flag it.

Rapid Check-Out

• Rule: Flag transactions where the time between login and payment is unusually fast.
• Example: If an account logs in and completes a transaction within 60 seconds, it could be a bot.

Incomplete Profiles

• Rule: Flag accounts with incomplete or suspicious profiles for review.
• Example: An account with no phone number and missing address details could be risky.

String Similarity on Personal Details

• Rule: Alert if personal details (like name, address, etc.) have a high similarity with known fraudulent accounts.
• Example: If a new account’s details are 90% similar to a previously flagged account, it gets flagged for review.

Multiple Accounts with Same Payment Method

• Rule: Flag if more than one account is linked to the same credit card or bank account within a short period.
• Example: If 3 different accounts are linked to the same credit card within 24 hours, flag all accounts.

Recurring Transactions with Even Amounts

• Rule: Transactions with even amounts that occur at regular intervals could be indicative of synthetic fraud.
• Example: If an account receives $100.00 exactly every seven days, flag it.

Cart Abandonment and Rapid Retry

• Rule: Flag repeated instances of cart abandonment followed by transaction attempts.
• Example: If an account abandons the cart three times but then tries to transact within the same hour, flag it.

Manual Entry of Credit Card Details

• Rule: Flag transactions where the credit card details are manually entered instead of swiped or inserted into a chip reader.
• Example: Manual entry of credit card for more than 3 consecutive transactions triggers a review.

Zero-Dollar Authorizations

• Rule: Flag accounts that perform excessive zero-dollar authorization checks.
• Example: More than 5 zero-dollar authorizations in a 24-hour period should be reviewed.

Use of Privacy Coins for Crypto Transactions

• Rule: Flag transactions that involve privacy coins (like Monero, Zcash).
• Example: If an account trades Bitcoin for Monero, flag it.

Multiple Failed Logins

• Rule: After a defined number of failed login attempts, flag and possibly lock the account.
• Example: If an account has 5 failed login attempts in 15 minutes, review for possible hacking attempts.

Free Email Services

• Rule: Extra verification required for accounts using free email service providers.
• Example: If the email address associated with an account is from a free service like Gmail, flag it for additional verification.

Overnight Transactions

• Rule: Flag transactions that occur during unusual hours, such as late at night or early in the morning.
• Example: Any transaction made between 2:00 AM and 5:00 AM local time is flagged for review.

Shipping and Billing Address Mismatch

• Rule: Flag orders where the shipping address and billing address do not match.
• Example: If an order is placed where the shipping address is in one country and the billing address is in another, flag it.

 

Limitations of Business Rules

False Positives

One of the key limitations is the generation of false positives. Since the rules are rigid, they can sometimes flag legitimate transactions as fraudulent, causing inconvenience to customers and requiring manual review.

Scalability

As a business grows, managing an ever-expanding set of business rules can become cumbersome. The complexity and volume of data may necessitate more sophisticated methods, such as machine learning algorithms.

Adaptability

Fraudsters are always evolving their methods. Business rules can quickly become outdated if not regularly reviewed and updated to adapt to new types of fraud.

Conclusion

Business rules serve as an essential component in the multi-layered domain of fraud prevention. Their simplicity, real-time monitoring capability, and ease of implementation make them a go-to method for organizations of all sizes. However, it’s crucial to understand their limitations and complement them with other fraud prevention techniques, such as advanced analytics and machine learning, for a comprehensive approach.

By integrating high-conviction business rules into a broader anti-fraud framework, organizations can better position themselves to mitigate risks while adapting to emerging threats. As the stakes continue to rise, a nuanced understanding of all the tools at one’s disposal becomes increasingly vital in the fight against financial crime.