AI-Generated Phishing in Finance: fraudulent emails are becoming more convincing

For years, many companies trained employees to identify phishing through simple warning signs: spelling mistakes, awkward translations, generic greetings, blurry logos, or messages that felt obviously suspicious.

That era is ending.

With the rise of generative artificial intelligence, fraudulent emails have become better written, more personalized, and much harder to distinguish from legitimate business communication. For Finance teams, this risk is especially critical: a single convincing email can lead to an unauthorized transfer, a fraudulent IBAN change, or the approval of a fake invoice.

The problem is not only that attackers can write better. It is that they can now write like a supplier, like a CEO, like a business partner, or like someone already involved in a real conversation.

According to the FBI IC3, Business Email Compromise attacks continue to affect companies of all sizes and were associated with more than $55 billion in exposed global losses between October 2013 and December 2023. At the same time, the Verizon DBIR 2025 highlights that the use of synthetic text in malicious emails has doubled over the last two years, showing how AI is already affecting the quality and scale of fraudulent messages.

What is AI-Generated Phishing?

AI-Generated Phishing is the use of artificial intelligence tools to create, adapt, or improve fraudulent messages used in phishing, spear phishing, Business Email Compromise, Vendor Email Compromise, or financial email fraud attacks.

In practice, this means an attacker can use AI to generate an email that:

Microsoft has also warned that AI is lowering the technical barrier for cybercriminals, enabling them to create convincing content such as phishing campaigns, fake websites, and other fraudulent schemes faster and at lower cost.

This completely changes how companies should think about phishing.

Phishing is no longer just a suspicious email. It can be a well-written, contextualized message, sent at the right time and aligned with a real financial process.

Why Finance teams are such attractive targets

Finance teams deal every day with payments, invoices, approvals, suppliers, bank details, and urgent requests. This creates the perfect environment for social engineering attacks.

An attacker does not need to compromise the entire company infrastructure. In many cases, manipulating one person at the right moment is enough.

For example:

With AI, these scenarios become more convincing because criminals can remove many of the traditional signs of fraud. The email no longer feels “strange”. It feels professional.

And that is exactly what makes it dangerous.

How AI makes financial phishing more convincing

Artificial intelligence did not create phishing, but it has dramatically increased its quality and efficiency.

In the past, many attacks were generic. The same email was sent to hundreds or thousands of people with little personalization. Today, an attacker can collect public information about a company, its employees, suppliers, and executives, then use AI to create highly targeted messages.

1. Emails without obvious mistakes

For a long time, spelling mistakes or poorly translated sentences were important warning signs. With generative AI, those mistakes disappear.

A fraudulent email can now have the same writing quality as legitimate corporate communication. It can use the right tone, the right level of formality, and even common expressions used in finance.

This makes it risky to rely only on visual or linguistic signs.

2. Personalization at scale

AI allows attackers to create different messages for different targets.

A Finance employee may receive an email about pending invoices. A manager may receive an approval request. A CFO may receive a strategic message about an acquisition or confidential payment.

The attack stops feeling mass-produced and starts feeling personal.

3. Imitation of tone and identity

With enough examples of public or compromised emails, an attacker can try to imitate the communication style of a real person.

They can write in a short and direct way like a CEO.

They can use a formal tone like a legal department.

They can sound helpful and technical like a regular supplier.

This imitation does not need to be perfect. It only needs to be credible enough for the victim to take the next step.

4. More natural multilingual attacks

Companies that work with international suppliers are especially exposed.

AI allows attackers to create convincing emails in English, Portuguese, Spanish, French, German, or other languages without the typical errors of older automatic translations. This makes attacks easier against companies with international operations, distributed teams, or foreign suppliers.

5. Better adaptation to context

One of the biggest risks is the ability to adapt messages to context.

If the attacker knows the company is closing the month, they can send an email about an urgent invoice.

If they know there is a relationship with a specific supplier, they can simulate a bank details update.

If they gain access to an old email thread, they can reply as if they were part of the conversation.

This is where AI-Generated Phishing overlaps with attacks such as Business Email Compromise, Vendor Email Compromise, and Thread Hijacking.

Examples of AI-Generated Phishing attacks in Finance

Fraudulent IBAN change

The Finance team receives an email from a known supplier:

The email looks normal. The tone is professional. There are no mistakes. The supplier exists. The invoice appears real.

But the IBAN belongs to the attacker.

Fake invoice with personalized language

The criminal generates a message adapted to the company’s history:

This type of message works because it does not feel too aggressive or too urgent. It feels administrative.

Fake CEO request

An employee receives a message that appears to come from the CEO:

AI can make this type of email more realistic, using language similar to executives and avoiding overly generic or suspicious phrases.

Fraud inside a legitimate conversation

In a more advanced attack, the criminal compromises an email account or uses information taken from an exposed mailbox. Then they reply inside a real conversation.

The email may say:

The finance team receives an email that appears to come from the CEO:

Because the message appears inside an existing thread, the victim is more likely to trust it.

Why traditional detection methods are no longer enough

Many companies still train employees to look for signs such as:

These signs are still useful, but they are no longer enough.

Modern financial phishing may have no mistakes. It may have no links. It may have no malicious attachments. It may not ask for passwords. It may simply request a financial action that appears normal.

That is why defense must stop depending only on how the email looks.

The main question should no longer be:

“Does this email look fake?”

The better question is:

“Does this request make sense within our financial process?”

Warning signs in AI-generated financial emails

Even when the email is well written, there are still behavioral and procedural signs that may indicate fraud.

A request should raise suspicion when it:

The key is to analyze the behavior behind the request, not only the quality of the text.

How to protect Finance teams from AI-Generated Phishing

Protection against this type of attack should combine technology, processes, and training.

1. Verification through a secondary channel

Any request to change bank details should be confirmed through an independent channel.

This means using a contact that was already registered before, not the phone number or email address included in the suspicious message.

If a supplier sends a new IBAN by email, the team should confirm it by phone with an official contact that has already been validated.

2. Dual approval for sensitive payments

Payments above a certain value, beneficiary changes, or urgent transfers should require additional approval.

This reduces the risk of a single person being manipulated by a convincing email.

3. Blocking financial changes by email only

Companies should avoid accepting IBAN changes, supplier details updates, or payment instructions based only on email.

Ideally, these changes should go through a secure portal, internal workflow, or formal validation process.

4. Monitoring similar domains

Attackers use domains that look similar to real ones to deceive employees.

For example:

Monitoring similar domains helps identify typosquatting and impersonation campaigns before they cause financial impact.

5. Email authentication and security

SPF, DKIM, and DMARC are important controls to reduce spoofing and domain abuse.

But they should not be seen as a complete solution. Many attacks come from compromised accounts or newly registered domains designed to look legitimate.

6. Finance-specific training

Generic phishing training is no longer enough.

Finance teams should be trained with realistic scenarios such as:

The goal is not only to teach people how to “spot phishing”. It is to teach them how to interrupt financial fraud.

7. Behavior-based detection

Security tools should analyze anomalies such as:

With AI-generated attacks, detection based only on keywords becomes less effective.

The role of internal culture

Technology is essential, but internal culture is just as important.

Many financial attacks work because employees feel pressured to act quickly, do not want to block processes, or are afraid to question requests from senior people.

A resilient company should create a culture where it is acceptable to pause, verify, and escalate.

No Finance employee should feel that they are slowing the business down by validating a payment instruction.

On the contrary, validation protects the business.

Conclusion

AI-Generated Phishing represents a new phase of financial email fraud.

Attackers no longer depend on poorly written messages or generic campaigns. With AI, they can create professional, personalized emails adapted to the real context of a company.

For Finance teams, this means that trusting what “looks legitimate” is no longer enough.

Defense must rely on clear processes, independent verification, continuous monitoring, specific training, and technology capable of detecting anomalous behavior.

In the end, the best protection against increasingly convincing fraudulent emails is not simply trying to determine whether the text was written by AI.

It is ensuring that no email, no matter how convincing it looks, can by itself change a payment, update an IBAN, or bypass the company’s financial process.