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Navigating the Complexity: Challenges in Integrating Rules Engines with API Orchestration

Updated: Nov 22, 2023





Introduction


Efficiently integrating rules engines with API orchestration is crucial in the context of fraud prevention for payments. Combining rules engines with API orchestration allows for real-time decision-making processes. In the context of fraud prevention, quick and accurate assessments of payment transactions are essential to identify and prevent fraudulent activities promptly as fraud patterns and tactics are constantly evolving.

The ability to dynamically adapt rules and orchestrate API calls based on the latest threat intelligence enhances the system's capability to detect and mitigate new and emerging fraud schemes effectively.

Payment systems often process a large number of transactions and therefore the combination of rules engines and API orchestration must support scalability, ensuring that the fraud prevention system can handle high transaction volumes efficiently without compromising performance. But combining rules with API orchestration can be challenging for several reasons:

  • Divergent Logic and Granularity: Rules Engine Logic vs. API Orchestration Logic: Rules engines often operate on a different logic than API orchestration tools. Rules engines typically use conditional statements and rule sets, while API orchestration involves coordinating the flow of data and actions across multiple APIs. Aligning these different logics can be complex.

  • Data Transformation and Mapping: Data Formats and Structures: Rules might be defined based on a certain data format or structure, and APIs may expect or produce data in a different format. Ensuring seamless data transformation and mapping between these different formats is essential but can be extremely hard to achieve.

  • Synchronous vs. Asynchronous: Rules might be designed for synchronous processing, where one rule triggers the next in a sequential manner. API orchestration often involves asynchronous processing, where responses may not be immediate, and managing the flow of execution becomes more complex.

  • Error Handling Strategies: Combining rules and API calls requires robust error handling mechanisms. APIs can fail for various reasons, and rules need to account for these failures and define appropriate actions. This increases the complexity of the overall system.

  • State Management: Stateful vs. Stateless: Rules engines may operate in a stateful manner (or run some sort of the SQL queries), while API orchestration might be designed to be stateless. Managing and synchronizing state across these components can introduce challenges.

  • Security Considerations:, Authentication and Authorization: Rules may not inherently consider security aspects like authentication and authorization, which are critical in API interactions (even more so, as you might want to combine API's from different providers).

  • Scalability and Performance: Scalability Challenges: Rules engines and API orchestration tools may have different scalability requirements. Combining them requires careful consideration of performance implications, especially as the system grows in complexity and scale. Your solution is as strong as the weakest link!

  • Integration Interfaces: Rules engines and API orchestration tools may use different integration interfaces and protocols. Ensuring seamless integration between these tools often involves developing custom connectors or adapters.

  • Versioning and Compatibility: Rules and APIs may evolve independently. Ensuring backward compatibility and version management becomes crucial to avoid disruptions in the system.

  • Testing and Debugging: Testing a system that combines rules and API orchestration can be complex. It requires thorough testing of rule logic, API interactions, and the overall system behavior. Rules engine outcomes are much easier to test, while API interactions and actual outcomes require mockup interfaces as specifically designed test cases.

Combining rules with API orchestration involves addressing differences in logic, data formats, processing paradigms, and more. It requires a well-thought-out architecture and careful integration to ensure a robust and scalable system.

FraudAway to the rescue


The FraudAway engine stands out in the market, offering a unique blend of capabilities. It is rooted in the Waylay Rules engine, and you can explore its features further by visiting the following link: "Waylay Rule Engine: One rules engine to rule them all." In essence, FraudAway is not just a rules engine; it seamlessly integrates both rule and orchestration functionalities. This innovative approach allows for the fusion of conditional statements, rule sets, and API outcomes within a single framework. Leveraging the asynchronous nature of the rules engine, FraudAway facilitates the aggregation of logical propositions, resembling the adaptability provided by a Finite State Machine - yet even more powerful!


Building Card Payment solution with FraudAway


In order to illustrate this solution, let's revisit one of the previous tutorials, where we want to combine rules, ML models and API responses: "The Future of Modern Fraud Detection with ML and Rules-Based Systems" . In the example below, you can see all these things in action: Conditional rules, integration with external API's as well as combination of ML models - all in one rule!

If you'd like to learn more, feel free to reach out to us to schedule a demo.


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