Digital Finance Transformation Model: Designing Risk and Control in Artificial Intelligence-Driven Accounting Systems

The accelerating adoption of artificial intelligence (AI) in accounting and financial management has introduced both transformative opportunities and complex risks. While AI-driven systems promise enhanced efficiency, predictive analytics, and real-time reporting, they simultaneously create vulnerabilities related to data integrity, model bias, algorithmic opacity, and regulatory compliance. This paper proposes the Digital Finance Transformation Model (DFTM), a conceptual framework designed to integrate risk management and control mechanisms into AI-enabled accounting systems. The model emphasizes a layered architecture of governance, technological safeguards, and adaptive controls that align financial innovation with trust and accountability. Key elements include algorithmic audit trails, explainability protocols, embedded compliance monitoring, and dynamic risk assessment tools capable of adjusting to evolving data environments. By positioning risk and control as integral to system design rather than afterthoughts, the DFTM provides organizations with a blueprint for embedding resilience, transparency, and ethical assurance into digital finance infrastructures. The framework also highlights the role of human oversight through augmented decision-making, where finance professionals complement AI outputs with contextual judgment and ethical considerations. Application of the DFTM ensures that AI-driven accounting systems do not only enhance efficiency but also preserve the fundamental principles of accuracy, reliability, and stakeholder trust. For regulators, the model offers insights into creating adaptable supervisory frameworks capable of keeping pace with technological innovation. For practitioners, it serves as a guide for integrating AI responsibly into core accounting functions such as auditing, reporting, fraud detection, and compliance monitoring. Ultimately, the DFTM positions risk and control as enablers rather than inhibitors of digital finance transformation, offering a structured pathway for reconciling innovation with accountability. By advancing a holistic approach that combines technology, governance, and human judgment, this study contributes to the evolving discourse on sustainable and trustworthy AI adoption in financial systems.