DFAS-AAP: AI Accountability Protocol in Financial Modelling
A Multi-Layered Governance Protocol for Explainable and Auditable Financial AI
Abstract
The increasing adoption of artificial intelligence (AI) in financial modelling has exposed critical gaps in accountability, traceability, and regulatory compliance. Traditional governance frameworks, although robust for deterministic systems, are ill-suited to address the probabilistic behaviours, override chains, and fiduciary risks inherent to black-box AI outputs.
This manuscript introduces the DFAS-AAP (AI Accountability Protocol), an integrated, multi-layered governance solution that enforces structured accountability, override transparency, and IFRS-aligned disclosure in AI-powered financial environments. DFAS-AAP combines five interdependent subsystems: DFAS-FEP for authorship and model classification, DAIF for override and explainability logic, DFAS-IFRS for fair value hierarchy and financial reporting compliance, DFAS-Governance for escalation chains and role mapping, and DFAS-CICP for audit-trail integrity and COSO-based internal control.
Central to the protocol are the Alaali AI Override Chain of Command (A-AOCC) and the Alaali Explainability Escalation Framework (AEEF), which bridge the accountability gap in operational AI environments. The framework is grounded in accountability theory, agency theory, and audit-assurance principles, and is demonstrated through AI use cases in valuation, stress testing, and ESG risk scoring. Mapped against the EU AI Act and IFRS standards, DFAS-AAP provides a doctrinal and operational blueprint for regulators, auditors, and institutions to ensure that AI use in finance is explainable, auditable, and aligned with international governance norms.
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