Banks are advancing into a new era of AI oversight as agentic systems shift from data summarisation to executing core financial tasks, prompting urgent questions about governance, transparency, and human oversight.
Banks are entering a new phase of artificial intelligence oversight as so-called agentic systems move from drafting text and summarising data to taking actions across core financial workflows. A white paper from Tech Mahindra argues that this shift will force a rethink of how quality assurance is done, because testing a model on its own will not show whether an agent can reliably process a loan, investigate fraud or rebalance a portfolio across multiple banking systems.
The company’s AI and digital transformation lead for financial services, Gopal Parasnis, said such systems should never operate in isolation in regulated finance. That warning reflects a broader concern now emerging across the sector: financial institutions are adopting AI more widely in fraud detection, customer service, compliance and operations, but their governance structures have not always kept pace. TechRadar has reported that banks and other firms are under growing pressure to make AI decision-making more transparent, especially as silos in data and technology can make accountability difficult.
What makes agentic AI different, according to the white paper, is that it can choose tools, pull information from multiple sources and alter its actions as conditions change. That means banks will need to test the full chain of decisions, system calls and transactions, not just whether the underlying model gives a plausible answer. The paper says teams must check whether agents use the right data, act in the correct order, stay within permissions and hand over to humans when needed. It also points to loan processing, payments, anti-money laundering, onboarding and investment management as areas where these systems could eventually handle end-to-end tasks.
The governance challenge goes beyond functionality. Tech Mahindra says every decision made by an agent must be traceable and logged, with records showing what information was accessed, which tools were used and why the system proceeded or stopped. That fits with Deloitte’s view that banks should extend existing AI risk frameworks to cover agent-specific risks such as tool misuse, action validity and outcome monitoring. It also echoes wider industry advice that clean data, clear documentation and integrated systems are essential if autonomous AI is to work safely in complex environments.
The paper also argues for human oversight, but says banks will have to prove that review steps actually work. It recommends testing whether escalation thresholds trigger properly, whether agents can bypass approval gates and whether reviewers receive enough context to intervene intelligently. The authors advise starting with lower-risk back-office tasks before moving towards customer-facing uses, then progressing through controlled pilots, multi-agent workflows and continuous monitoring. Tech Mahindra also says early deployments can deliver sizeable productivity gains and cut false positives in fraud detection, but only if institutions test the behaviour of the full system rather than the model at its core.
Disclaimer: This article is intended to inform and educate, not to recommend or endorse any financial product, investment or strategy. Please consider your own financial circumstances and seek professional advice where appropriate before making financial decisions.
