How AI fraud risk for financial institutions is reshaping risk management

Artificial intelligence is changing how banks and other financial institutions operate, compete and serve customers. It is also changing the fraud landscape. Generative AI, in particular, is making it easier for scammers to deploy convincing fraud schemes at greater speed, scale and believability. Risk leaders and other C-suite leaders at financial institutions need to understand where controls, processes and people may be most exposed to generative AI-enabled fraud.

Across the financial services ecosystem, recent incidents point to a consistent theme: AI does not replace traditional fraud techniques; rather, it enhances them. AI fraud risk for financial institutions refers to the increased exposure created when AI enables fraud schemes to scale faster, appear more credible and bypass traditional controls.

Established fraud typologies such as impersonation, false documentation and social engineering remain prevalent, but are now amplified by AI.

How AI is changing fraud risk for banks

In a survey of professionals at banking and financial services companies in the United States, the United Kingdom and several other countries, conducted by security services firm BioCatch, “51% of organizations lost between $5 million and $25 million in total to AI-based or AI-driven threats in 2023.”

Also in 2023 and 2024, FinCEN said in an alert about fraud associated with GenAI tools that the agency had “observed an increase in suspicious activity reporting by financial institutions describing the suspected use of deepfake media in fraud schemes targeting their institutions and customers.”

The use of AI has only accelerated in the years since, as generative AI has made financial fraud easier to scale. As fraud becomes more sophisticated, institutions are increasingly leveraging technology to strengthen verification and monitoring earlier in the process, especially in vendor onboarding and master data maintenance.

Managing AI fraud risk for banks and credit unions

While no single solution can address generative AI‑enabled fraud risk in financial services, several categories of emerging tools and capabilities can help financial institutions manage AI-related fraud risk:

1. Vendor identity verification tools

AI-generated documents make it easier to impersonate legitimate vendors or submit convincing requests to update banking information, creating risk well before any payment is made. Solutions that validate business legitimacy using third-party data sources (e.g., business registries, tax IDs and ownership structures) help confirm that a vendor is real before onboarding. Some tools also validate whether submitted documentation aligns with known records.

2. Document authenticity and fraud detection

AI-enabled tools can analyze invoices, bank letters and onboarding documents for signs of manipulation or synthetic generation (e.g., metadata inconsistencies, formatting anomalies and image tampering). This is becoming increasingly important as GenAI improves document quality. Institutions need to reassess how they validate source authenticity, not just document completeness.

3. Behavioral and pattern-based monitoring

While transaction monitoring remains important, it often catches issues after funds have left the institution. Increasingly, fraud activity begins earlier in the process. This means that, rather than focusing only on transactions, institutions are using analytics to monitor activity patterns such as:

• Frequency of vendor changes
• Timing of updates (e.g., end of quarter, after hours)
• Users initiating and approving changes
• Speed of approval workflows

These signals can highlight anomalous behavior and control override risks before a payment is executed.

4. Workflow-integrated controls

People remain central to fraud risk and fraud prevention. Many schemes still rely on social engineering, but AI tools make impersonation attempts more credible. Embedding controls directly into systems (e.g., required dual approval for bank detail changes, enforced call-back checkpoints, automated flags for high-risk updates) reduces reliance on manual judgment, limits the effectiveness of social engineering and increases consistency.

A holistic understanding of AI fraud risk

AI-enabled fraud sits at the intersection of technology, operations and culture. As such, addressing it requires more than adding new tools. It calls for a clear understanding of where processes are most vulnerable, how an institution enforces its controls and whether employees feel empowered to challenge unusual requests.

While AI does not fundamentally change fraud typologies, it materially alters the speed, volume and believability of attacks. For C-suite leaders, the goal is to ensure that the organization’s defenses are keeping pace with how fraud is evolving. GenAI has changed the economics of fraud. Institutions that respond by strengthening fundamentals will be better positioned to manage that change.