Architecting Future Finance: Secure, Intelligent, and Scalable Big Data Banking with Mlops on Aws

The rapid digital transformation of banking in Sri Lanka creates both opportunities for data-driven services and challenges in security, compliance, and system scalability. This study proposes a cloud-native big data architecture on Amazon Web Services (AWS) integrated with Machine Learning Operations (MLOps) to address these challenges for a leading Sri Lankan commercial bank. Using a single-case study design, we assessed the bank's legacy infrastructure via interviews with nine stakeholders (IT, data science, corporate-level Executives and business users. The proposed modular architecture integrates heterogeneous data sources into an S3-based data lake, supports batch and streaming ingestion (AWS Glue, Kinesis), offers analytical serving via Athena/Redshift, and embeds an ML Ops pipeline (ML flow, Sage Maker, EKS) with feature store and monitoring (Prometheus). Security and governance are enforced through IAM, KMS, Lake Formation, CloudTrail and Guard Duty to meet local data residency and GDPR-aligned controls. Empirical evaluation of the pilot reports a 30% reduction in IT operational costs, a 4× improvement in data processing throughput, and a reduction in fraud detection time from hours to under 5 minutes, while enabling weekly model updates and reproducible model lineage. We discuss implementation challenges, stakeholder training needs, and regulatory coordination. Contributions of this work include:  
1.    A practical AWS-based reference architecture tailored to Sri Lankan banking constraints.
2.    An integrated MLOps pattern for regulated environments
3.    An evaluation demonstrating measurable operational and analytic gains.
The paper concludes with recommendations for phased cloud migration, governance checklists, and future research directions, including quantitative ROI studies and multi-bank comparative analyses.