How CFOs Balance Leaner Teams With Rising Fraud Demands

Morgan Stanley announced last month it would reduce its global workforce by about 2,500 employees, or roughly 3%. Other companies such as Block, Home Depot and Salesforce have recently announced significant layoffs, with leadership often pointing to the capabilities of AI to handle tasks previously managed by white-collar professionals.

German embedded finance platform Solaris is also cutting 20% of its workforce, while Amazon cut roughly 16,000 jobs, HSBC is reportedly weighing cuts of up to 10% of its own global talent base, and Oracle slashed thousands of jobs as well.

Enterprise accounts payable (AP) and accounts receivable (AR) functions—traditionally labor-intensive and heavily manual departments—have been among the beneficiaries of the corporate landscape’s efficiency-driven workforce transformation.

Still, as teams shrink and workflows accelerate, a more subtle risk is emerging.

Payment fraud is evolving faster than the systems and teams designed to stop it. Automation, synthetic identity construction and artificial intelligence (AI)-generated deception are raising the bar for what it takes to identify and stop fraudulent activity.

For CFOs, the challenge then is becoming one of navigating the growing tension between operational efficiency and financial control in an environment where adversaries are leveraging the same technologies of automation, AI and data synthesis that companies have come to rely on.

See also: Fraud Is Knocking Louder on the CFO’s Door

The New Fraud Landscape is Faster, Smarter and Harder to Detect

The timing of these cuts is occurring just as the nature of fraud itself is evolving in ways that make detection more complex.

Fraud is no longer a game of isolated criminals using stolen credit cards. It has become an industrialized activity, supported by tools and techniques that mirror those used by legitimate businesses. Automation allows fraudsters to test thousands of variations of an attack in real time, optimizing their methods with a level of speed and precision that would have been impossible a few years ago.

Synthetic identities, which are combinations of real and fabricated personal information, have now become a cornerstone of many fraud schemes. These identities can be nurtured over time, building credit histories and transaction records that make them difficult to distinguish from legitimate users. When deployed, they can bypass traditional verification systems with alarming effectiveness.

Layered on top of this is the growing use of generative AI. Fraudsters are using AI to create convincing phishing messages, deepfake audio and even synthetic customer service interactions. These tools lower the barrier to entry while increasing the sophistication of attacks, enabling less experienced fraudsters to execute schemes that once required specialized skills.

“Checks have been centered around determining if activity is machine-driven or human-driven,” Christine Hurtubise, vice president of artificial intelligence and machine learning at FIS, said. “That paradigm is shifting as AI agents are able to replicate human activities.”

Findings in “Identity at Scale: Where KYC/KYB Touchpoints Create (or Contain) Agent Risk,” a new report from PYMNTS Intelligence and Trulioo, underscore the impact that continuous lifecycle management can have in defending against AI-powered fraud.

More here: Why Identity Silos Are Failing in the AI Era

Lean Teams, Expanding Attack Surfaces

The shift toward leaner finance teams has been driven by necessity and enabled by technology. Automation platforms now handle invoice processing, payment approvals and reconciliation tasks that once required multiple full-time employees. While these systems deliver measurable efficiency gains, they also introduce new dependencies—and vulnerabilities.

In smaller organizations or downsized departments, individuals may hold overlapping responsibilities across vendor onboarding, invoice approval and payment execution. This consolidation can create blind spots that sophisticated attackers are quick to exploit.

The result is an expanded attack surface, not necessarily in terms of system architecture, but in operational exposure. Fraud is increasingly targeting the “gray areas” of finance processes, the moments where automation hands off to human judgment, or where oversight is assumed rather than explicitly enforced.

Ultimately, the challenge for CFOs is one of balance. The drive for efficiency is both necessary and beneficial, but it cannot come at the expense of resilience. Payment fraud is no longer a peripheral risk; it is a strategic concern that intersects with technology, operations and governance. This may require a more proactive stance, where fraud prevention is embedded into transformation initiatives from the outset, rather than addressed as an afterthought.