Automated Transaction Processing System with Machine Learning for Financial Services

The client faces complexity in managing high-volume payment transactions with manual oversight, leading to delays and increased operational costs. Existing models are fragmented across multiple systems, perform calculations every few minutes, and lack integration, resulting in inefficiencies and regulatory compliance risks. There is a critical need for a centralized, real-time decision-making solution powered by machine learning to streamline transaction handling and reduce manual intervention.

A large neobank or digital financial institution with a customer base exceeding 1 million, processing billions in transactions with a focus on regulatory compliance and operational efficiency.

• Develop a cloud-agnostic, end-to-end transaction decision-making platform that automates fraud detection and risk assessment in real-time.
• Integrate multiple existing machine learning models into a single scalable system to improve maintainability and performance.
• Establish real-time data ingestion and processing pipelines to reduce transaction latency from up to 5 minutes to under 250 milliseconds.
• Automate deployment and model management through MLOps pipelines, including versioning, CI/CD integration, and model serving.
• Reduce operational expenses by migrating data calculations from legacy databases to efficient ETL pipelines and streamlining data flow.
• Enhance compliance with sanctions, AML, and fraud regulations through continuous, real-time analysis and decision logging.
• Achieve measurable improvements such as increasing customer satisfaction scores and growth in customer base.

• A unified API layer for external systems to submit transaction data and retrieve risk assessments.
• An integrated feature store to manage ML features and pre-calculated data accessible via API endpoints.
• Runtime environment for serving multiple ML models with version control, using technologies such as BentoML.
• Real-time data ingestion and processing pipelines utilizing Apache Kafka, Debezium CDC, and Apache Flink for stream and batch processing.
• ETL pipelines to prepare and transform data from internal and external sources, reducing database load.
• A centralized ML model repository with versioning and management, supporting continuous model updates.
• Automated deployment pipeline with CI/CD for cloud-agnostic environment using containerization and orchestration (Docker, Kubernetes).
• Secure handling of sensitive data with anonymization and compliance adherence.

• Internal banking operational and analytical databases for data retrieval
• External APIs for credit scores and other external feature data
• Existing transaction processing systems for API integration

• Latency: Under 250 milliseconds per transaction request
• Throughput: Support up to 1 million requests per month
• Scalability: Cloud-agnostic architecture capable of scaling horizontally
• Security: Encrypted data handling and strict anonymization protocols
• Reliability: Minimize system downtime and ensure high availability
• Maintainability: Modular design supporting easy updates and model retraining

Implementing this unified, real-time ML-powered transaction processing platform is expected to significantly improve operational efficiency, reduce manual review efforts, and lower operational costs. The system should enable reaction times from several minutes down to milliseconds, decrease latency, and support regulatory compliance. Anticipated results include a 20% increase in customer acquisition, a 35-point improvement in customer satisfaction scores, and more streamlined fraud and risk management processes, driving growth and competitiveness in the financial services sector.