Scalable Cloud Models for Banking Transaction Monitoring
Keywords:
Digital banking transaction monitoring: a cloud data management and processing system model; a bank transaction monitoring system that adheres to regulatory requirements; cloud processing of banking data; banking data digital transformation; real-time transaction monitoring of digital banking transactions; near real-time monitoring of banking transactions; rule-based monitoring of banking transactions; machine-learning-based monitoring of banking transactions; full-text data storage for banking transactions.Abstract
Electronic banking offers new opportunities for credit unions and other community financial institutions. By allowing members to bank 24 hours a day from virtually anywhere in the world, the benefits of electronic banking must be weighed against the potential for increased fraud and illicit transactions. Regulatory requirements mandate that as a result of the Money Laundering Control Act of 1986, banks need to have a formal anti-money laundering program that includes procedures to monitor electronic banking activity. Cloud computing technologies provide scalable computing resources at a reduced operational cost, but their use for anti-money laundering systems has not yet been demonstrated. The characteristics of digital banking transactions indicate these systems may benefit from cloud-based resources. Appropriate cloud architectures and data ingestion and transformation methods are proposed to support real-time rule-based compliance system detection and machine-learning-based anomaly detection. Cloud processing scalability creates processing capacity for compliance requirements without negatively impacting the underlying economic advantage.
Cloud computing has emerged as a disruptive technology enabling scalable and affordable data processing in a rapidly evolving environment, and organizations have seized the opportunity to move data storage and processing outside the enterprise firewall. Scalable data processing can be a significant advantage for organizations with irregularly occurring, high-volume access patterns. For compliance monitoring and operational reporting, however, regulatory requirements necessitate monitoring and reporting within a defined period. Such rigid time constraints reduce the potential operational cost benefit of cloud-scale processing resources. Digital transaction characteristics suggest compliance monitoring and anomaly detection can be separated and deployed in a hybrid processing model. The rule-based detection models of the operational system provide anomaly detection ground truth and a source of training data for machine-learning models. Cloud processing enables a scalable architecture to meet current and future capacity demands.
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