Using a Rule Engine to Tailor Your Antifraud Strategy

Using a rule engine for antifraud can provide many benefits for businesses. By allowing users to define custom rules and conditions based on data from third-party service providers and their own data, a rule engine can help identify and prevent fraudulent transactions and protect against operational risk.

The Advantages of Integrating with Third-Party Service Providers

One of the key advantages of using a rule engine for antifraud is its ability to integrate with third-party service providers, such as SEON, Maxmind, and FraudLabs, to access a wealth of data and information about potential fraud. While these service providers can provide valuable rules and insights, it is important to use them primarily as data providers and evaluate the data on your own using a rule engine. This can help ensure that your antifraud strategy is tailored to your specific business needs and risks.

Another advantage of using a rule engine for antifraud is its ability to verify IP addresses against block lists, such as AbuseIPDB and Project Honeypot. By checking IP addresses against these lists, businesses can identify and block transactions from known malicious actors and protect against potential fraud.

Another benefit of using a rule engine for antifraud is its ability to calculate velocities on your own data to establish antifraud rules. By analyzing trends and patterns in your own transaction data, businesses can identify unusual or suspicious behavior and create custom rules to flag and prevent fraudulent transactions.

Preventing Fraud with Custom Rules for IP address

Here are five examples of rules that could be implemented using a rule engine to prevent fraud in ecommerce based on IP addresses:

1. Block transactions from IP addresses that have been associated with high levels of fraud in the past. This rule could be based on data from third-party service providers, such as Maxmind or FraudLabs, or on your own data analysis.
2. Require additional authentication for transactions from IP addresses that are not typically used by the customer. This rule could be based on the customer's previous transaction history or on data from third-party service providers.
3. Block transactions from IP addresses that are known to be associated with malicious actors. This rule could be based on data from block lists, such as AbuseIPDB or Project Honeypot.
4. Require additional authentication for transactions from IP addresses that are not in the same country or region as the customer's billing or shipping address. This rule could be based on the customer's address information or on data from third-party service providers.
5. Block transactions from IP addresses that are associated with high levels of velocity or unusual activity. This rule could be based on your own data analysis, using velocity calculations to identify suspicious behavior.

Preventing Fraud with Custom Rules for E-mail Address

Here are five examples of rules that could be implemented using a rule engine to prevent fraud in ecommerce based on email addresses:

1. Block transactions from customers with disposable or temporary email addresses. This rule could be based on data from third-party service providers, such as SEON or FraudLabs, or on your own data analysis.
2. Require additional authentication for transactions from customers with newly-created email addresses. This rule could be based on the customer's previous transaction history or on data from third-party service providers.
3. Block transactions from customers with email addresses that are known to be associated with malicious actors. This rule could be based on data from block lists or databases of known fraudulent email addresses.
4. Require additional authentication for transactions from customers with email addresses that are not in the same domain as the customer's billing or shipping address. This rule could be based on the customer's address information or on data from third-party service providers.
5. Block transactions from customers with email addresses that are associated with high levels of velocity or unusual activity. This rule could be based on your own data analysis, using velocity calculations to identify suspicious behavior.

Custom Rules for Browser Information and Antifraud

Here are five examples of rules that could be implemented using a rule engine to prevent fraud in ecommerce based on browser information:

1. Block transactions from customers using browsers that are known to be associated with high levels of fraud. This rule could be based on data from third-party service providers, such as WhatIsMyBrowser or FraudLabs, or on your own data analysis.
2. Require additional authentication for transactions from customers using browsers that are not typically used by the customer. This rule could be based on the customer's previous transaction history or on data from third-party service providers.
3. Block transactions from customers using browsers that are known to be associated with malicious actors. This rule could be based on data from block lists or databases of known fraudulent browser information.
4. Require additional authentication for transactions from customers using browsers that are not commonly used in the customer's country or region. This rule could be based on data from third-party service providers, such as WhatIsMyBrowser, or on your own data analysis.
5. Block transactions from customers using browsers that are associated with high levels of velocity or unusual activity. This rule could be based on your own data analysis, using velocity calculations to identify suspicious behavior.

Custom Rules for Physical Addresses and Antifraud

Here are five examples of rules that could be implemented using a rule engine to prevent fraud in ecommerce based on physical addresses:

1. Block transactions from customers with fake or invalid addresses. This rule could be based on data from third-party service providers, such as Ekata or Google Places API, or on your own data analysis.
2. Require additional authentication for transactions from customers with newly-created or recently-changed addresses. This rule could be based on the customer's previous transaction history or on data from third-party service providers.
3. Block transactions from customers with addresses that are known to be associated with high levels of fraud. This rule could be based on data from third-party service providers or on your own data analysis.
4. Require additional authentication for transactions from customers with addresses that are not in the same country or region as the customer's billing or shipping information. This rule could be based on the customer's address information or on data from third-party service providers.
5. Block transactions from customers with addresses that are associated with high levels of velocity or unusual activity. This rule could be based on your own data analysis, using velocity calculations to identify suspicious behavior.

Conclusion

In conclusion, using a rule engine for antifraud can provide many benefits for businesses, including the ability to quickly and easily integrate with third-party service providers, verify IP addresses and email addresses against block lists, and calculate velocities on your own data to create custom rules. By using third-party service providers primarily as data providers and implementing custom rules in a rule engine, businesses can effectively identify and prevent fraudulent transactions and protect against operational risk.

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