Using a Rule Engine to Tailor Your Antifraud Strategy

Using a rule engine for antifraud gives businesses more control. Users can define custom rules and conditions based on data from third-party service providers and their own data. This helps identify and prevent fraudulent transactions, and reduce operational risk.

The Advantages of Integrating with Third-Party Service Providers

One key advantage of using a rule engine for antifraud is that it can integrate with third-party service providers, such as Maxmind and FraudLabs, to access a wide range of data about potential fraud. These providers can supply useful signals and insights, but it makes sense to treat them mainly as data providers and evaluate that data yourself in a rule engine. That helps keep your antifraud strategy aligned with your business needs and risk profile.

Another advantage of using a rule engine for antifraud is that it can 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 reduce fraud exposure.

Another benefit of using a rule engine for antifraud is that it can calculate velocities on your own data to define 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 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.
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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

Using a rule engine for antifraud gives businesses practical control. It supports fast integration with third-party service providers, checks IP addresses and email addresses against block lists, and calculates velocities on your own data to create custom rules. By treating third-party services mainly as data providers and implementing your own rules in a rule engine, businesses can identify and prevent fraudulent transactions more effectively, and reduce operational risk.

Frequently asked questions

How should I combine IP, email, browser, and address signals before declining an order?

Group signals into a few action bands, and decline only when independent indicators point to the same transaction being risky. A single mismatch should usually prompt additional authentication or review, especially for a new customer or a shared network. Set each band against confirmed fraud and legitimate orders by market and transaction value, since one cutoff can over-block particular customer groups.

How should I handle transactions from shared IP addresses?

Treat a shared IP as context, not proof of fraud. Offices, mobile carriers, public Wi-Fi and households can put legitimate customers behind the same address, so look for account, device or transaction evidence that links the order to suspicious activity before declining it. If the IP signal is concerning but inconclusive, use additional authentication or review and track the false-positive rate.

What should an antifraud rule do when a provider’s data is missing or delayed?

Choose the fallback action before launch. For a low-value order, you might continue with the signals you do have, then monitor the results. For a higher-risk transaction, route it to additional authentication or review. Do not treat missing data as a clean result, and do not automatically decline every order because a provider is unavailable, since that can create a broad interruption for legitimate customers.

What should happen when two antifraud rules recommend different actions?

Set an explicit priority for the actions, such as decline over additional authentication, and make sure the more severe action requires evidence strong enough to justify it. Check overlapping conditions against real transactions before launch, including cases where one rule flags an order and another clears it. Ambiguous precedence can make decisions inconsistent and harder to tune when fraud or customer friction changes.

Decisimo decision engine

Put these checks into a decision flow

Create rules for credit, fraud or collections, then run them on live decisions.

See pricing

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