How AI and ML Will Help Banks Build the Next-Gen Workforce

Many financial institutions see talent trouble coming from two directions: the fierce competition for in-demand tech talent such as software engineers and IT workers, and the growing swath of baby boomers who are reaching retirement age. 

Taken together, they put the industry at risk of coming up shorthanded in headcount. Indeed, the financial and business services industries are expected to face the largest shortfall of workers of any industry by 2030, a Korn Ferry global report found.

When seasoned workers leave, they take decades of institutional knowledge and know-how with them, creating a growing operations talent gap. And as more and more veteran employees walk out the door, pressure is building to fill their seats. 

Banks are having trouble attracting and hiring Gen Z workers, who will make up 27% of the workforce by 2025. Younger workers have much to contribute but are skeptical of the banking industry, according to a survey by EY. Banks must modernize their employee experience, including roles and learning models, to attract the right talent.

As the pace of technological change increases, a bank’s ability to evolve at scale and engage customers will rely on attracting and retaining the necessary talent, says Nicole Carrillo, managing director,  financial services industry, at Workday. “Without the right talent to leverage all of this new technology, change is simply not going to come at the pace banks want it to,” Carrillo says.

The answer, however, isn’t simply to ramp up recruiting efforts and hope to fill every seat. Banks need to think more broadly about workforce development, Carrillo says. That means proactively sizing up skills gaps and then rolling out targeted upskilling and retraining efforts. Artificial intelligence (AI) and machine learning (ML) can support these HR activities—while also freeing up time for more strategic work. 

“When it comes to talent and AI and ML, the opportunity for banks is about building the capacity to create more value,” Carrillo says. “These technologies are going to help across multiple functions, adding value in new ways and at entirely new scales. In fact, it’s already happening.”

Many bank leaders seem to understand this. An overwhelming majority (86%) believe they need to leverage AI and ML to keep their business competitive, the Workday AI IQ: Insights on Artificial Intelligence in the Enterprise report found. 

Greater Visibility, Proactive Talent Management

Leaning on AI and ML technologies is not new for the banking industry. The ability to rapidly detect anomalous and possibly fraudulent credit card transactions, for one, stems from ML capabilities. And AI can be seen in the proliferation of “How can I help you?” pop-up chat boxes on company websites.

These kinds of discrete, customer-facing forays are typical of the banking sector’s first AI and ML wave. The new wave of tech is focused more on internal functions. It’s an opportunity to evolve the industry in powerful ways. 

Increasing the automation of manual and repetitive transactions and processes will give employees more time to focus on strategic decisions. But for HR leaders looking to address persistent talent challenges, AI and ML can also assist with skills mapping and predictive workforce analytics.

Large data models can capture skills requirements role by role, team by team, and function by function. That sets the table for AI- and ML-supported capabilities to identify both current and emerging skills gaps, Carrillo says. HR leaders can then discover powerful insights and proactively troubleshoot talent challenges.

A system could flag heightened turnover in a certain type of role and employee profile—for example, young software engineers. “Then you could assess the rationale for them leaving, and do more than just intensify hiring efforts,” Carrillo says. “You can dig into people’s reasons for leaving and start to change roles and responsibilities to address pain points.”

An HR platform that integrates AI and ML can also identify current deficits and predict near-term talent needs, optimize resource allocation, and empower inclusive talent management. As an example, if an employee keeps logging off early from work, that could be a sign of an imminent resignation. This predictive insight enables HR to anticipate skills gaps and vacancies before the organization starts sweating, Carrillo notes. 

“The value here is about identifying anomalies and making suggestions,” Carrillo says. “Predictive analytics supported by AI and ML can surface near-term workforce events and trends, allowing leaders to plan for emerging gaps and needs.”

The Great Knowledge Transfer

One near- and medium-term workforce trend facing banks is already quite clear. The workforce is rapidly aging. By 2030, 150 million jobs will shift to workers aged 55 and older. The wave of retiring baby boomers is well underway, threatening daily operations as organizations lose experienced workers with decades of accrued knowledge.

AI and ML can help HR navigate this generational talent challenge as well, Carrillo says. Banks won’t be able to hire enough people to take all the operational reins, much less fully train them, before veterans say goodbye. Solving this knowledge transfer challenge can be a valuable proving ground for generative-AI-supported knowledge capture.

“These machines can learn by following employee actions, so to speak, to document operating procedures and make training recommendations,” says Carrillo, noting that anything the tools suggest should be validated by people. “And these materials will likely be better than me writing down everything I do because these tools can actually learn what I do.”

In terms of managing the retirement waves, the upshot for banks is huge. “This is going to reduce the knowledge gap between outgoing, highly informed people and those who are just getting their footing in operations,” Carrillo adds. Imagine a chatbot that can support a new employee with reminders not to forget certain procedural steps or to obtain certain approvals before proceeding. 

Trained on huge amounts of data, “these tools are going to help people do their jobs more effectively,” Carrillo says. 

Launching a Skills-Based Movement

AI and ML are poised to revolutionize how people and technology coexist at banks, including both HR functions and individual roles. With so much change on the horizon, leaders must answer two crucial questions.

“What do you want the future of work to look like in an AI- and ML-enabled environment?” Carrillo says. “And how will you progress toward that target? Leaders need to put aside old habits and assumptions, and embrace new ways of thinking and working.”

A skills-based (rather than job-based) approach to workforce development is gaining steam. It focuses less on degree credentials and linear career progression. In an era of persistent talent gaps, this approach, supported by upskilling and retraining, can pay dividends. AI and ML can support these activities in the background, turning employee and skills requirement data into strategic advantage.

In the big picture, what’s at stake is the ability to seize valuable opportunities for productivity and innovation. “Ultimately, AI is going to reshape so many day-to-day activities in banks—and it’s going to happen everywhere,” Carrillo says. “Organizations are going to need people with new and different skills.”

If banks can tap the potential offered by new AI and ML technologies, Carrillo says they might even achieve something that today feels a bit far-fetched—a talent surplus. “If firms can create opportunities for talent to work on innovative projects and interact with customers in new ways, it could attract smart people with in-demand skills,” Carrillo explains. “If banks can step up, we could see really excited workforces.”