Fraud and Compliance
Combating AI fraud: why finance leaders must act now
Key insights
- AI fraud is accelerating faster than traditional controls can adapt
- Deepfakes, synthetic documents, and AI-personalized phishing are now mainstream threats
- Only 35% of finance teams rate their fraud prevention as effective
- Fraud is increasingly multi-channel and harder to detect
- CFOs must lead cross-functional fraud prevention strategies (people, controls, governance)
What is AI fraud and why is it a growing threat?
AI fraud refers to the use of artificial intelligence to enable or enhance fraudulent activity, including impersonation, synthetic documents, and automated social engineering attacks. AI is no longer just transforming productivity. It is rapidly reshaping fraud. Across Australia and New Zealand, finance leaders are facing a new reality where AI-driven scams are faster, cheaper to deploy, and far more convincing than traditional fraud attempts. What makes this moment different is not just the sophistication of the technology, but the speed at which it is outpacing existing controls.
A recent survey of finance, audit, and risk professionals identified “new technology outpacing controls” as the leading driver of fraud. This creates a critical inflection point for CFOs, particularly as deepfakes, synthetic documents, and AI-powered impersonation move from fringe risks to everyday operational threats. Deepfakes are AI-generated audio or video designed to convincingly mimic real people, often used in fraud schemes to impersonate executives or employees in real time.
Why AI fraud matters now for finance leaders
Companies globally report losing 7.7% of revenue to fraud, underscoring the material impact this has on growth, margins, and trust.
AI-driven fraud is escalating at the same time organisations are becoming more digitally connected. Remote work, cloud systems, mobile approvals, and complex supplier networks have expanded attack surfaces. At the same time, AI tools have become widely accessible, lowering the barrier for criminals to launch highly personalised scams at scale.
The result is a fraud environment where:
- Attack entry points are multiplying
- Fraud tactics are evolving faster than policy updates
- Finance teams are under pressure to approve transactions quickly
- Traditional detection methods are proving insufficient
What is the cost of AI fraud for businesses?
Only 35% of finance teams globally rated their fraud prevention as effective in 2025, a sharp decline from the previous year. This gap between risk and readiness leaves organisations exposed to preventable losses.
AI fraud does not only lead to direct financial loss. It creates second-order risks that finance leaders cannot afford to ignore:
- Increased compliance exposure if fraudulent payments breach internal controls
- Productivity loss as teams spend more time manually checking transactions
- Reputational damage when fraud incidents become public
- Erosion of employee confidence in systems and leadership
Five AI fraud realities reshaping finance risk
1. AI impersonation is redefining trust
Deepfake audio and video are now realistic enough to deceive employees in live interactions, including video calls. These attacks exploit urgency, authority, and familiarity, putting intense pressure on staff to act quickly.
2. Synthetic documents are flooding finance workflows
Generative AI can fabricate invoices, receipts, supplier records, and expense claims in seconds. These documents often include synthetic data designed to bypass traditional checks, making detection significantly harder.
3. Business Email Compromise has evolved
BEC scams now leverage scraped public data and AI-driven personalisation to time attacks precisely. Email, SMS, and voice channels are increasingly combined to create highly plausible, multi-stage fraud attempts.
4. Shadow AI is creating blind spots
Unsanctioned use of external AI tools by employees introduces low-visibility risks, increasing the likelihood of data exposure and manipulation without governance oversight.
5. Fraud is becoming multi-channel
The most concerning trend is the combination of multiple AI techniques into a single attack. A fake invoice may be followed by a deepfake voice note or a phishing message, dramatically increasing the chance of success.
| AI fraud type | What it is | Finance impact |
|---|---|---|
| AI impersonation | Deepfake audio/video mimicking real people | Fraudulent approvals via perceived authority |
| Synthetic documents | AI-generated invoices, receipts, records | Hard-to-detect fake or altered financial docs |
| Business Email Compromise (BEC) | Impersonation via email and messaging | Payment diversion and data theft |
| Shadow AI | Unapproved employee use of AI tools | Data exposure and governance blind spots |
| Multi-channel fraud | Combined attack across email, voice, messaging | More convincing and harder to detect fraud |
Why CFOs are central to combating AI fraud
Combating AI fraud cannot sit solely with IT or internal audit. The CFO is uniquely positioned to lead because the threat spans payments, expenses, procurement, treasury, compliance, and people. Finance leaders also play a critical role in shaping culture, governance, and decision-making norms.
Effective responses require more than tools. They demand alignment across finance, IT, legal, and HR, supported by training, clear policies, and leadership that normalises verification and challenge.
Learn moreHow can organisations prevent AI fraud?
This guide outlines a comprehensive framework built around three pillars:
- People, focusing on training, awareness, and a fraud-aware culture
- Controls, strengthening approvals, audits, and verification processes
- Governance, ensuring AI tools are deployed responsibly with strong oversight
Used well, AI can also become a powerful ally. When combined with human judgement, AI-driven detection can flag anomalies, identify deepfakes, and surface patterns that manual reviews miss.
Get the full framework for finance leaders in Australia and New Zealand
AI fraud is a shapeshifting threat, but it is not unstoppable. Finance leaders who act now can reduce exposure, strengthen trust, and future-proof their controls.
👉 Download the full guide, Combating AI fraud: A guide for finance leaders, to explore the complete frameworks, controls, and governance models CFOs need to stay ahead of AI-driven fraud.
Frequently asked questions
What is AI fraud in finance?
AI fraud is the use of artificial intelligence to enable or automate fraudulent activity, including impersonation, synthetic documents, and AI-powered social engineering attacks. It is rapidly reshaping fraud by making scams faster, cheaper to deploy, and more convincing than traditional methods.
Why is AI fraud becoming a major risk for finance leaders?
AI fraud is increasing as organisations become more digitally connected and attackers gain access to widely available AI tools. Remote work, cloud systems, mobile approvals, and complex supplier networks have expanded attack surfaces, while fraud tactics are evolving faster than policy updates.
What are the most common types of AI-driven fraud?
Key AI fraud risks include AI impersonation using deepfake audio and video, synthetic documents such as invoices and receipts, advanced Business Email Compromise (BEC) scams, shadow AI usage, and multi-channel fraud that combines multiple attack methods.
What is Business Email Compromise (BEC)?
Business Email Compromise (BEC) is a type of fraud where attackers impersonate trusted individuals or vendors using email and other digital channels. AI now enables these scams to be more personalised and harder to detect.
What is shadow AI and why does it matter?
Shadow AI refers to the unsanctioned use of external AI tools by employees. It creates blind spots for organisations and increases the risk of data exposure and manipulation without governance oversight.
Why are traditional fraud detection methods no longer enough?
Traditional detection methods are proving insufficient because fraud tactics are evolving faster than policy updates. Only 35% of finance teams globally rate their fraud prevention as effective, leaving organisations exposed to preventable losses.
What are the consequences of AI fraud beyond financial loss?
AI fraud can lead to increased compliance exposure, productivity loss as teams manually verify transactions, reputational damage when incidents become public, and erosion of employee confidence in systems and leadership.
What should CFOs focus on to combat AI fraud?
CFOs are central to combating AI fraud because it spans payments, expenses, procurement, treasury, compliance, and people. Effective responses require alignment across finance, IT, legal, and HR, supported by training, clear policies, and strong governance.
What is the recommended approach to combating AI fraud?
A comprehensive framework is built around three pillars: people (training and awareness), controls (stronger approvals and verification), and governance (responsible use of AI tools and oversight).
