APPLYING MACHINE LEARNING TO HIGH-VOLUME BANKING PLATFORMS: FROM TRANSACTION DATA TO PREDICTIVE RISK INTELLIGENCE
DOI:
https://doi.org/10.15680/d312b736Keywords:
Machine Learning, Banking Platforms, Transaction Data Analytics, Predictive Risk Intelligence, Fraud Detection, Credit Risk Modeling, Anti-Money Laundering (AML), Explainable AI, Real-Time Risk Scoring, MLOpsAbstract
High-volume banking platforms generate massive streams of transactional data across payments, lending, cards, and digital channels. Effectively transforming this data into actionable risk insights is critical for fraud prevention, credit decisioning, anti-money laundering (AML), and regulatory compliance. Traditional rule-based systems, while reliable, struggle to scale and adapt to rapidly evolving risk patterns and transaction volumes. Machine Learning (ML) provides a data-driven approach to identify complex patterns, predict emerging risks, and enhance decision accuracy in both real-time and batch processing environments. This article presents a generalized architectural and analytical framework for applying machine learning to high-volume banking platforms. It explores transaction data pipelines, feature engineering strategies, model selection for various risk domains, real-time and batch scoring mechanisms, explainability requirements, and MLOps practices. The paper also discusses governance, security, and regulatory considerations essential for deploying ML-based risk intelligence in production banking systems. By integrating scalable data architectures with responsible machine learning practices, banks can convert raw transaction data into predictive risk intelligence while maintaining trust, transparency, and compliance
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