AI, risk and restructures: What’s really driving financial services hiring?
AI, risk and restructures: What’s really driving financial services hiring?
If you only followed the headlines, you’d think the financial services sector is being driven by large-scale AI transformation. It’s not.
Across Australia, banks, insurers and fintechs are navigating a mix of regulatory scrutiny, cost-cutting, evolving cyber threats, fragmented transformation programs, and targeted investment in areas like data, cyber and platform modernisation. While AI is part of the conversation, it has yet to materially reshape organisations at scale.
Instead, hiring is being influenced by a series of overlapping shifts:
- Heightened regulatory scrutiny and risk accountability
- Ongoing restructuring to fund transformation and reduce cost bases
- Smaller teams expected to deliver broader, more technical outcomes
- AI embedding into workflows rather than driving standalone transformation
We sat down with our in-house Financial Services specialists to unpack what they’re seeing across risk, cyber, infrastructure, software and AI and how it’s shaping hiring.
Regulation is driving more work
The most consistent driver across the market right now is regulation.
According to Chris Huggett, Practice Director – Financial Services at Talent Sydney:
“There’s a lot more pre-emptive work happening right now around risk and governance, but not all of it is directly driven by AI. Previously, it was reactive, particularly post-Royal Commission. Now organisations are trying to get ahead of issues before they happen.”
That shift is being reinforced by public scrutiny.
Ongoing issues with the ASX have triggered intense regulatory pressure around operational resilience and governance standards.
“That level of scrutiny inevitably drives transformation, particularly around resilience and risk culture,” says Elliott Howard, Account Director.
At the same time, AI-enabled fraud is becoming more sophisticated and scalable from deep fakes and voice cloning to synthetic identities and large-scale loan scams.
What this means for hiring: Demand is increasing for governance, resilience and risk capability, embedded within broader programs rather than standalone initiatives.
AI is being contained, not scaled
AI is being adopted in controlled, low-risk environments.
“We’re seeing smaller-scale initiatives in niche pockets with specific use cases,” says James Bertollo, Account Manager. “They’re what you’d consider minor implementations like customer service, internal automation, agent-based workflows, but nothing like enterprise-wide transformation.”
The constraint is risk.
“For banks, using AI on private customer data is still a major concern,” James adds. “There’s a real fear of making a mistake that is both costly and leads to regulatory consequences.”
The caution is being reinforced by a combination of factors:
- Increasing regulatory scrutiny around data handling and decision-making
- The potential for AI-driven errors at scale
- A lack of clear, consistent frameworks for AI governance
As a result, organisations are taking a segmented approach. Rather than pursing a single, enterprise-wide AI program, most are:
- Deploying AI within specific business units
- Ringfencing higher-risk data environments
- Building internal guardrails as they go
“I don’t think you’ll see one singular transformation program around AI,” says Chris. “It will sit within different areas of the business, with different levels of control depending on the risk.”
What this means for hiring: Steady but fragmented demand for AI capability, creating pockets of hiring rather than large-scale workforce shifts.
AI’s immediate impact is at the operational level
While AI isn’t driving enterprise transformation, it is already reshaping day-to-day execution.
“In cyber, what used to take one or two days can now be done in 30 minutes using AI agents,” says Elliott.
This pattern is consistent across functions: faster analysis, reduced manual effort and compressed delivery timelines. And it’s already changing expectations of output, speed and team structure.
What this means for hiring: Expectations of output are increasing and teams are being designed around speed and efficiency, raising the bar for individual capability.
Hiring demand is concentrating at the execution layer
The clearest signal of change has been an increasing demand of talent at the execution layer.
According to James:
“Over the past two years, we’ve seen very senior AI roles put in place to set strategy and frameworks and kick off initiatives. In a lot of cases, those roles have been brought in externally, while the middle layer—product owners, project managers—has largely stayed the same and been upskilled.
Where we’re seeing the highest demand, and the most volume, is at the doing level. These are the engineers and practitioners who are actually building and implementing AI. That’s consistent across banking, financial services and insurance. Those are the roles we’re seeing come through most frequently.”
At the same time, AI capability has become a baseline expectation.
“Every role now has ‘AI’ as either a nice-to-have or a must-have,” says Chris.
Roles are being redefined:
- Engineers are expected to work end-to-end
- Technical roles require stronger business engagement
- Pure specialisation is declining
“You won’t ever hire a pure Python developer anymore,” says James. “You need someone who can design, build, implement and engage with stakeholders. With specialisations like coding becoming less central, the core defining skill required of an end-to-end software engineer is now what our clients call a ‘solution mindset’.”
What this means for hiring: Highest demand for hands-on, end-to-end practitioners who can deliver outcomes not just define them.
Cost pressure is reshaping team structures
With budgets tightening, teams are becoming leaner.
Over the past 12–18 months, major financial institutions have reduced headcount significantly.
Elliott explains:
“Last year, we saw one of the biggest years in redundancies and cost-cutting across the banks, probably since the GFC. Whether that’s translating into continued investment in tech transformation, it’s more likely a cost-cutting exercise and what’s now widely described as ‘AI washing’.”
As a result, organisations are being forced to do more with fewer people, emulating the operating models and ways of working of technology companies.
“We’re seeing smaller teams and higher expectations,” says Chris. “People are being asked to cover multiple functions.”
This means combining roles that were previously separate, such as:
- Project Manager / Product Owner = Project Manager + Business Analyst + Tester
- Engineer = Engineer + Tester + UI Designer + sometimes Business Analyst
- Scrum Master = Project Manager + Business Analyst
But this introduces delivery risk.
“If you’re asking someone to test their own work, it creates risk and ultimately sets the program up for failure,” Chris explains. “But ultimately, it comes down to cost constraints.”
What this means for hiring: There’s greater demand for multi-skilled individuals holding higher accountability, but also increased risk where separation of duties is lost.
Some roles are already in decline
Not all roles are being affected equally. Some are already in clear decline.
Project Managers, Business Analysts, Agile Coaches, Scrum Masters, and layers of middle management.
“It’s a very challenging time to be a Project Manager in the market right now,” says Elliott. “We’re seeing hundreds of applicants for a single role. We recently had a general Project Manager role receive 200–300 applications within 48 hours, and many of those candidates have been looking for work for months.”
This reflects a shift away from large, program-based transformation towards smaller, incremental initiatives delivered within existing product and engineering teams.
“Agile isn’t a standalone role anymore,” says Chris. “It’s just part of the job.”
What this means for hiring: Roles focused on coordination, governance and process are being absorbed into more technically capable, end-to-end positions.
The offshore delivery model is coming under pressure
AI is also beginning to challenge traditional offshore delivery models.
Historically, offshore teams have been used to handle repetitive developer work and high volume support tasks. The same areas where AI is now most effective.
“There are conversations happening about whether AI reduces the need for offshore models,” says Chris. “Because that type of work is increasingly automatable.”
While this shift is not fully realised at this stage, it is becoming a recurring theme in how organisations are thinking about future operating models.
If this direction continues, the implications are significant:
- Reduced reliance on offshore execution capacity
- Greater emphasis on onshore, higher-value roles
- A shift from production-based work to orchestration and oversight
It has the potential to reshape how work is distributed across global teams.
Hiring strategies are diverging across subsectors
Not all financial services subsectors are responding to these pressures in the same way.
Different subsectors are taking materially different approaches to growth, cost and risk, and this is flowing through into how they hire.
In banking, the focus remains on consolidation, cost control and internal efficiency.
“Banks are focused on consolidating,” Chris shares. “It’s more about optimising what they already have.”
This means tighter headcount control, more selective hiring and a focus on roles that directly support priority initiatives.
In contrast, super funds are still actively pursuing growth through M&A and market expansion.
“We’re seeing more movement in the super space,” Chris notes. “There are still opportunities for growth and acquisition.”
This is creating demand in areas tied to integration, transformation and scaling capability.
Meanwhile, fintechs are operating with a different set of constraints. After a period of rapid expansion post-COVID, the focus has shifted towards sustainability.
“Fintechs are being much more measured now,” says Elliott. “It’s less about expansion and more about maintaining cost base and growing sustainably.”
As a result, fintech hiring is more cautious, with fewer speculative hires, greater emphasis on immediate value and tighter alignment to commercial outcomes.
Across all three, the underlying pattern is consistent: Hiring has become more targeted, more deliberate and more closely tied to business priorities.
Hiring is becoming more disciplined
Despite shifts across technology, regulation and operating models, the core drivers of hiring in financial services remain consistent.
“Transformation initiatives, regulatory pressure and remediation following failure. Those core reasons for hiring aren’t going away any time soon,” says James. “What changes is the lens and right now that lens is AI.”
While AI is influencing how work is delivered, how teams are structured, and what is expected from individual roles, it is not replacing the underlying reasons organisations hire.
What has changed is:
- Greater scrutiny on where value is created
- Higher expectations of individual capability
- Less tolerance for roles that do not directly contribute to outcomes
Instead of a completely new hiring model, the result is much more disciplined one where every role is expected to deliver measurable value.
The bottom line
AI may be changing how work gets done, but it’s not what’s driving hiring.
Across financial services, hiring is being dictated by risk, cost and accountability.
Organisations are becoming more selective, roles are being redefined, and expectations of individuals are increasing.
For leaders, the implication is clear: Hiring decisions are now more closely tied to measurable value than at any point in the past decade.
If you’re navigating cost pressure, regulatory change or shifting expectations, our Financial Services recruitment specialists can share what’s happening across the market. Reach out to discuss how these shifts are impacting your hiring priorities.