Fraud Detection Microservices for OSS/BSS Agility
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Solution Overview
Problem
Traditional fraud detection systems are monolithic and lack the agility to provide continuous integration and continuous delivery, with serialized interfaces to OSS/BSS, which impedes real-time updates and feature velocity, and are not well-suited for microservices-oriented API-driven architectures.
Innovation Solution
Implementing a fraud detection system as a service using microservices that can be instantiated on OSS/BSS, allowing for dynamic updates and real-time monitoring of data streams, enabling continuous integration and continuous delivery, and leveraging machine learning for fraud detection and rule refinement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a monolithic fraud detection system is used, then system stability and simplicity are maintained, but the system lacks agility for continuous integration and delivery, and real-time updates are impeded
Solution Approach 1:
The fraud detection system is divided into independent microservices that can be developed, deployed, and updated separately. Each microservice handles a specific fraud detection function, allowing continuous integration and delivery without affecting the entire system. This segmentation enables agile updates while maintaining overall system stability.
Solution Approach 2:
The system transitions from a static monolithic architecture to a dynamic microservices architecture where services can be instantiated, updated, and scaled independently. This dynamic structure allows real-time updates and continuous deployment, resolving the contradiction between agility and complexity.
2Productivity
If serialized interfaces are used to connect fraud detection systems with OSS/BSS, then system simplicity is maintained, but continuous integration and real-time updates are unable to be provided
Solution Approach 1:
The interface architecture is segmented into multiple independent communication channels between microservices and OSS/BSS systems. This allows parallel processing and real-time updates without serialization bottlenecks, improving productivity while managing complexity through modular interface design.
Solution Approach 2:
The system implements continuous integration pipelines that enable uninterrupted development, testing, and deployment of fraud detection microservices. This continuous action allows real-time updates to be pushed to OSS/BSS interfaces without system downtime, resolving the contradiction between update speed and interface complexity.
3Adaptability or versatility
If a monolithic architecture is used, then data processing is centralized, but the system cannot provide continuous integration and continuous delivery
Solution Approach 1:
By segmenting the fraud detection system into independent microservices, each service can be updated and deployed independently through continuous integration pipelines. This eliminates the need to wait for entire system updates, reducing deployment time while enabling continuous delivery capabilities.
Solution Approach 2:
The system implements continuous integration pipelines that perform automated testing, validation, and preparation of microservice updates before deployment. This preliminary action ensures that updates are ready for immediate deployment, reducing time loss while maintaining continuous delivery capability.
Data Source
AI summary
A fraud detection as a service system is provided that can be implemented via one or more microservices that can be instantiated on an operation support system and/or business support systems. Each fraud detection microservice instantiated can have one or more fraud detection models and sets of rules for a particular type of incoming data stream or transaction and/or interaction data. In this way, there can be more than one fraud detection microservice operating on the OSS/BSS allowing each fraud detection microservice to be dynamically updated in real time, provide continuous integration and continuous delivery of services, and work with a particular data flow, providing for an optimal fraud detection process.