Cloud-Native ML Pipelines for AI-Powered Payment Risk Scoring
Keywords:
Payment Risk Scoring Systems, Cloud-Native Big Data Architecture, Real-Time Risk Evaluation, Payment Service Providers, Financial Transaction Analytics, Machine Learning–Based Risk Metrics, Near-Native Response Time, Data Generation And Ingestion, Event-Driven Processing, Batch Data Processing, Cloud-Native IoT Systems, Risk Scoring Services, Lifelong Machine Learning, CI/CD Pipelines For ML, Data Governance And Privacy, Zero-Trust Security Architecture, Observability And Monitoring, Scalable Financial Platforms, Payment Analytics Data Lakes, Intelligent Financial Risk Management.Abstract
The growing volumes of payment transactions have placed significant burden on payment service providers or financial institutions to evaluate the risk profiles of customers swiftly. Various risk-related metrics of customers can be obtained from the data related to their payment transactions, and the recent development of machine learning-based modeling approaches allows these metrics to be modeled as a risk score. However, the shoe has on the other foot as providing such services has become a competitive game between players leading to the demand for near-native response time in serving customer requests. The services, therefore, require a cloud-native big data architecture where the data generation, ingestion, processing and modeling operations can be carried out at huge scales and low response and lead times while integrating the required principles of data governance and privacy. Cloud-native IoT systems collect vast volumes of data daily that forms the data lake for processing. Data processing includes both batch and event-driven processing. Batch processing is used for generating risk-related metrics where as event-driven processing is used for carrying out risk scoring. The ML models behind risk metrics are provided as services and lifelong learning is done by using CI/CD pipelines. Firewall and observability adopt a zero-trust policy to promote not just security but also privacy and governance compliance. Robust architecture has enormous readability and scalability features for tackling such diverse services. The entire architecture is illustrated with an example in the domain of payment service providers using a risk scoring system proposed in the previous work.
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