ai
Governing Agentic AI in FinTech
- Source
- arXiv — Computers and Society
- Published
- Last verified
- 13 Aug 2026
- Confidence
- High
- Evidence
- Original document retained
- Reading time
- 1 min
- Country
- International
- Relevant to
- Research & Evidence, Risk & Compliance, Board & Governance, Finance & Investment, Policy & Regulation, Technology & Data
Executive summary
What happened, and why should leadership care?
Financial institutions are increasingly delegating critical decision-making to agentic AI systems, which operate with minimal oversight. This report highlights a significant gap in the governance of such AI, particularly within the FinTech sector, termed the 'Verifiability Gap.' This gap represents the shortfall between the required verification for delegated authority and the actual explainability and reproducibility of AI decisions. A new multilevel governance theory for agentic AI is proposed and partially tested, suggesting that the primary governance challenge is verifiability rather than AI capabilities.
Why this matters
Why is this strategically important?
The increasing reliance on autonomous AI in critical financial operations poses substantial governance and risk management challenges. Understanding and addressing the 'Verifiability Gap' is crucial for maintaining trust, ensuring regulatory compliance, and mitigating potential financial and reputational risks associated with AI-driven decisions. Establishing robust governance frameworks for agentic AI is essential for the responsible and effective integration of these technologies into the financial ecosystem.
Key insights
What should be noted from the evidence?
- Financial institutions are delegating consequential decisions to agentic AI systems that operate with limited human oversight.
- The governance of agentic AI in FinTech is currently under-researched.
- The primary constraint in governing agentic AI is identified as verifiability, not the system's capabilities.
- The 'Verifiability Gap' is defined as the discrepancy between the verification needed for delegated authority and the explainability/reproducibility of AI decisions.
- This gap is indexed to specific verifiers, evidentiary standards, and audit lag.
Evidence and confidence
How far can this assessment be trusted?
High confidence. Named institution, original document retained and analysis corroborated.
Analysis is prepared editorially by Aziz Shuaib Ausi. The original publication remains the authoritative record, and executive judgement remains entirely human.
Source
Where does this originate?
Reported by arXiv — Computers and Society · International. This briefing summarises the publication for executive use; the document itself is not reproduced here.
Read the original publication