This study investigates the role of automated controls and streamlined compliance in managing risk in digital finance institutions. Through a mixed-methods approach, combining quantitative analysis and qualitative insights from leading Indian digital finance companies (CRED, ZEST MONEY and PHONE PAY), the research examines the effectiveness of advanced technologies in enhancing risk management practices. The study found strong positive correlations between the adoption of automated controls and key performance metrics, including the fraud detection rate (0.68), regulatory reporting accuracy (0.72), and overall risk management effectiveness (0.75). Regression analysis revealed that automated control implementation was the strongest predictor of risk management effectiveness. A comparative analysis of different digital finance models highlights the varying challenges and benefits of implementing these technologies. This study proposes a holistic, future-proof framework that integrates AI, machine learning and blockchain for real-time risk monitoring, automated regulatory reporting and dynamic compliance management. This framework addresses the limitations of the current fragmented approaches and offers a scalable solution that is adaptable to evolving regulatory landscapes. The findings of this study have significant implications for practitioners and policymakers, emphasizing the need for investment in strong automated systems and adaptive regulatory frameworks. While acknowledging the limitations in sample size and longitudinal scope, this research provides a foundation for future studies and offers practical insights for enhancing risk management in the rapidly evolving digital finance sector. The study concludes that effective integration of automated controls and streamlined compliance is crucial for ensuring the resilience, integrity and sustainable growth of the digital finance ecosystem.
