Finance governance researcher Kailash Sadangi says that AI’s rapid integration into finance has outstripped the development of oversight frameworks, resulting in an expanding model-risk gap.
AI adoption is moving faster than the controls designed to govern it, leaving a growing gap in model-risk accountability across finance functions.”— Kailash Nath SadangiMELBOURNE, VICTORIA, AUSTRALIA, October 2, 2026 /EINPresswire.com/ — Fresh findings from finance governance expert Kailash Sadangi highlight a widening chasm between the speed of AI deployment in corporate finance and the ability of internal controls to keep up.
KPMG’s 2026 global survey reveals that active AI usage in finance departments has more than doubled over two years; over three-quarters of organisations now leverage AI for financial planning, reporting, and commercial analysis, and 71% report that it meets or exceeds ROI expectations. Sadangi’s research notes that while finance functions appear transformed externally, deployment has far outpaced the control frameworks meant to govern it.
BCG’s separate research on financial institutions found that although 71% rated their own AI capabilities at mid-tier maturity or higher, an objective assessment showed only about 25% had genuinely woven AI into strategic operations. Sadangi’s analysis identifies this gap between perceived and actual governance maturity as a critical issue: internal controls, audit trails, and sign-off processes built for human-generated financial data do not automatically extend to model-generated outputs, especially as generative and agentic AI evolve from single-output tools into more autonomous, multi-step decision chains.
Regulators are also responding. In April 2026, the US Federal Reserve, the OCC, and the FDIC issued SR 26-2, a major update to model risk management guidance that had remained largely unchanged for over a decade. The updated guidance explicitly excludes generative and agentic AI from its formal scope, citing the rapid pace of technological change. Sadangi’s research cites this exclusion as evidence that oversight frameworks are still catching up to tools already in daily use across finance. Separate global research on 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 also 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.
References used:
* KPMG International, “2026 The Decision Advantage: AI in Finance” — XFB
