The quiet power shift reshaping financial services – with AI at its core

By Adrien Chenailler (pictured), Global Director for AI Industry Solutions, Financial Services, Cloudera

In financial services, the biggest transformations aren’t happening on trading floors or branch networks. They’re unfolding quietly, in the ways banks and insurers are weaving Artificial Intelligence (AI) and real-time data into the very fabric of their operations – reshaping how decisions are made, how risk is managed and how customers are engaged.

The pace of change is also accelerating. According to Gartner, over half (58%) of finance functions are now using AI – up from just 37% the year before. That’s more than adoption; it’s a sign of a power shift that regulators, board members and customers are all watching closely, as authority begins to move from people to machines.

What makes this shift so significant is not the technology itself, but the movement of authority. Decision-making is no longer solely in the hands of analysts, credit officers or compliance teams. Increasingly, it rests with algorithms capable of approving insurance claims or blocking fraudulent payments in real time. The transfer of power goes far beyond efficiency – it raises questions about trust, accountability and control.

The conversation now turns to the dynamics behind this shift:  the demand for speed and accuracy at scale, the regulatory guardrails designed to keep AI accountable, and the trusted data as the foundation to move forward responsibly.

Speed and Accuracy Are the New Battleground

If there is one force that is truly propelling the shift of authority from people to machines, it’s speed. Financial services now run on sub-second decision windows that humans simply cannot match. In fraud detection, for example, banks have just 0.1 seconds to decide whether a transaction is legitimate. That’s not just a technical challenge; it’s an existential one. Fail to act fast enough and fraud slips through; act incorrectly and you risk blocking legitimate customers.

Speed also transforms customer engagement. A traveller opening their banking app at the airport might instantly receive a travel insurance offer if the banking system detects they’re overseas. Delay that insight by even a day, and the moment and opportunity is gone.

We’re already seeing real-world impact. Axis Bank uses real-time nudges to drive uptake of instant loans – but only because the offer is accurate and timely. In Australia, a major insurer is streamlining claims processing using AI and real-time data, cutting turnaround times from weeks to days or hours.

The common thread is clear: in financial services, milliseconds now separate success from failure. Customer trust can be won or lost in an instant.

Regulation as the Counterweight

If speed is the driver, regulation is the counterbalance that ensures the transfer of authority doesn’t spiral into blind automation. Emerging governance frameworks are now as influential as the technology itself, reshaping how organisations design, train, deploy, and oversee AI systems. From Australia’s CPS 234 and CPS 230 to Singapore’s MAS AI guidelines and Europe’s sweeping AI Act, a new regulatory landscape is rewriting the rules of engagement. Accountability, transparency, and resilience are no longer optional; they’re mandated across every stage of the AI lifecycle.

This isn’t about ticking compliance boxes. Robust governance demands a fundamental rethink of how data is managed across hybrid environments, on-premises and cloud alike. It calls for built-in mechanisms that ensure visibility, explainability, and oversight from day one. Retrofitting compliance after deployment simply won’t cut it. As Gartner suggests, many agentic AI projects will fail due to unclear goals or a lack of trust. Regulation sharpens the urgency to address these gaps by forcing clarity, transparency, and aligning AI efforts with meaningful business outcomes.

Trusted Data as the Foundation of Responsible Authority 

However, even with regulation in place, split-second decisioning is only as good as the data it runs on. If the underlying data is incomplete, siloed or opaque, the same AI that delights customers one moment can expose the institution to risk the next.

This is why secure data platforms matter just as much as the front-end experience. They support real-time processing while meeting data residency and privacy requirements. Tools like Cloudera Octopai Data Lineage let banks trace every input that feeds into an AI decision, so when regulators or customers ask “why”, there’s a clear and defensible answer.

Ultimately, trusted data is what turns AI from a risky black box into a reliable partner. It ensures that when authority shifts from people to machines, it does so on solid, transparent foundations.