The integration of predictive analytics with core banking systems represents a transformative approach for financial institutions seeking to enhance operational efficiency, risk management, and customer experience. This article examines key considerations in this integration process, drawing insights from the PenFed Credit Union’s PANGEN Project for credit card processing and the International Finance Corporation’s iPortal and iDesk applications. This article explores essential factors, including data quality frameworks, system interoperability challenges, scalability requirements, regulatory compliance protocols, model transparency approaches, and stakeholder trust development. By addressing these critical elements, financial institutions can successfully implement predictive analytics solutions that drive innovation while maintaining security, compliance, and customer confidence in an increasingly data-driven banking landscape.
Keywords: banking systems integration, data governance, model transparency, predictive analytics, regulatory compliance
