Finance Governance Researcher Kailash Sadangi Flags Growing Model-Risk Gap in AI-Powered Finance Functions

AI adoption in finance is outpacing governance controls, creating a growing model-risk gap, says finance governance researcher Kailash Sadangi.

MELBOURNE, VICTORIA, AUSTRALIA, October 2, 2026 /EINPresswire.com/ — New analysis from finance governance researcher Kailash Sadangi points to a widening gap between how quickly artificial intelligence has been adopted in corporate finance functions and how well internal controls have kept pace.

According to KPMG’s 2026 global survey, active AI use across the finance function has more than doubled in two years, with over three-quarters of organisations now using AI in financial planning, reporting and commercial analysis, and 71% saying it is meeting or exceeding ROI expectations. Kailash Sadangi’s research notes that while finance functions appear transformed on the surface, deployment has moved considerably faster than the control frameworks designed to govern it.

Separate research by BCG on financial institutions found that while 71% rated their own AI capabilities at mid-tier maturity or above, objective assessment showed only around 25% had genuinely integrated AI into strategic operations. Sadangi’s analysis identifies this gap between perceived and actual governance maturity as a central concern: internal controls, audit trails and sign-off processes built for human-generated financial data do not automatically extend to model-generated outputs, particularly as generative and agentic AI move from single-output tools into more autonomous, multi-step decision chains.

Regulatory bodies have also begun responding to the shift. In April 2026, the US Federal Reserve, the OCC and the FDIC issued SR 26-2, a major revision of supervisory guidance on model risk management that had remained largely unchanged for over a decade. The updated guidance explicitly excludes generative and agentic AI from its formal scope, citing the pace at which these technologies are evolving. Kailash Sadangi’s research cites this exclusion as evidence that oversight frameworks are still catching up to tools already in daily use across finance functions. Separate global research into generative AI in financial institutions reaches a similar conclusion, noting that firms need to document AI use cases, conduct model audits and embed human oversight in the absence of unified global AI regulation.

Sadangi’s analysis further points to research on AI-driven cyber threat intelligence in financial institutions, which identifies “shadow use” of AI tools outside formal institutional controls, along with missing security monitoring and audit-ready evidence for AI models themselves, as a recurring failure mode — meaning the models finance teams increasingly rely on are often the least-audited part of the process.

“The risk isn’t that AI in finance produces obviously wrong numbers — most of the time it doesn’t,” said Kailash Sadangi. “The risk is that when a model drifts, hallucinates or degrades in accuracy, the controls designed to catch human error may not be built to catch model error, and few finance functions have clearly assigned who is accountable for closing that gap. Closing it doesn’t require finance leaders to become AI engineers — it requires model audits treated as seriously as financial audits, documented human sign-off points built into AI-assisted workflows, and clear ownership of model risk sitting somewhere specific, not spread thinly across IT, risk and finance with nobody formally accountable.”

Kailash Sadangi is a senior finance executive with Group CFO experience across Australia, the Middle East and international markets, and a doctoral researcher examining CFO-centred governance of AI-enabled decision-making.

Sources referenced:
* KPMG International, “2026 The Decision Advantage: AI in Finance” — https://kpmg.com/kpmg-us/content/dam/kpmg/pdf/2026/kpmg-ai-in-finance.pdf
* BCG data on AI maturity gap in financial institutions — https://www.360factors.com/blog/ai-finance-risk-and-compliance/
* Federal Reserve/OCC/FDIC, SR 26-2 model risk management guidance (2026) — https://arxiv.org/pdf/2607.04103
* “Generative AI in Financial Institutions: A Global Survey of Opportunities, Threats, and Regulation” — https://arxiv.org/pdf/2504.21574
* “Security Barriers to Trustworthy AI-Driven Cyber Threat Intelligence in Finance” — https://arxiv.org/pdf/2603.23304

Kailash Sadangi
IGNADAS CONSULTING LLP
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