Integrating AI-Driven Test Orchestration in RegTech Platforms for Compliance Automation in Banking Systems

Abstract

The issue of regulatory compliance in the banking sector has become quite complicated as the types of regulatory changes, emerging typologies of financial crime, and growing demands in terms of transparency and auditability continue to grow. RegTech solutions are now a key technological reaction, utilizing automation, analytics, and artificial intelligence (AI) to focus on the automation of compliance procedures. Nevertheless, even with the current developments in digital compliance, the process of testing these workflows, especially the ones that use AI-based models and rule-based engines, is still mostly manual, siloed, and hard to scale. The current paper suggests an AI-based test orchestration framework embedded into RegTechs. The framework will automate the test generation process, streamline the test execution process with risk-based prioritisation, and constantly monitor system behaviour to identify anomalies. The study identifies the enhancement of compliance accuracy, operational agility, and release agility through a conceptual assessment using banking scenarios. The proposed architecture will help to convert the conventional reactive compliance methods into permanent, proactive, and automated assurance frameworks to fit the current digital banking environments.