Key Payment Fraud Red Flags for Banks & Fraud Teams: Explained

Payment fraud red flags are the warning signs and unusual patterns that help banks and financial institutions identify potentially fraudulent payments or accounts before a transaction is initiated.

According to the European Banking Authority (EBA) and the European Central Bank (ECB), payment fraud continues to evolve, with total fraud losses across the European Economic Area rising to €4.2 billion in 2024.

For fraud teams at banks and financial institutions, spotting these red flags early is the difference between blocking a payment and chasing funds that have already left the account.

In this article, we'll explain what the most common red flags for payment fraud are, why they often go unnoticed, and how you can identify them before funds are lost.

Key Takeaways

• Look for patterns, not just suspicious transactions

Payment fraud typically develops over time, which can be seen through changes in customer behavior and account activity. Looking at the full sequence of events instead of individual payments helps detect fraud earlier.

• Monitor changes in account behavior and customer profiles

Sudden changes in payment habits, inactive accounts becoming active, multiple profile updates, or a series of small test transactions can all indicate preparation for fraud.

• Evaluate both the sender and the recipient

Monitoring recipient accounts for signs of money mule activity and using counterparty intelligence can help identify high-risk payment destinations before funds are transferred.

• Modern payment fraud requires continuous monitoring

Static rules often struggle to keep up with evolving fraud tactics and real-time payments. Continuously analyzing account intelligence, pre-fraud signals, and cross-channel activity gives fraud teams more time to identify threats before losses occur.

• Use a platform that detects fraud before the payment happens

Acoru helps financial institutions detect payment fraud earlier by combining data across existing systems, continuously classifying accounts, evaluating both senders and recipients, and using AI and consortium intelligence to uncover fraud patterns that traditional transaction monitoring may miss.

What Is Payment Fraud?

Payment fraud refers to financial transactions carried out through unauthorized access to a customer's account or by deceiving customers into authorizing payments themselves.

In some cases, fraudsters take control of an account and initiate the payment without the customer's knowledge. In others, they use scams or social engineering to convince customers to willingly approve the transaction.

Some of the most common types of payment fraud include:

• Account takeover (ATO)
• Chargeback fraud
• Stolen card fraud
• Money laundering
• Identity theft
• Refund fraud
• Bank identification number (BIN) attacks
• Card testing
• Triangulation fraud
• Authorized push payment (APP) fraud

Most Payment Fraud Red Flags Slip Past Detection Systems

Despite the availability of many fraud‑detection tools, most red flags often slip past detection systems and fraud teams. Several factors contribute to this problem:

1. Alert Fatigue from Rule-Based Systems

Most fraud detection systems rely on predefined rules, such as flagging large payments or transfers to a new beneficiary.

While these rules are useful, they often generate a large number of alerts, many of which are legitimate activity. As a result, fraud teams spend valuable time reviewing false positives, making it easier for genuine threats to be overlooked.

2. Fraud Tactics Evolve Faster than Rules

Fraudsters constantly change their tactics to avoid detection, while rule-based systems are typically updated only after fraud teams identify new attack patterns, create new rules, and test them before deployment. By the time these updates are in place, fraudsters may have already changed their approach by:

• Changing transaction amounts
• Using different payment methods
• Spreading activity across multiple accounts

This makes it difficult for static rule-based systems to keep up.

3. Fragmented Systems and Siloed Teams

Fraud signals are often spread across different systems and departments, which makes it challenging to see the full picture.

One team may detect unusual account activity while another investigates suspicious payments, but without sharing data, those signals remain disconnected. Consequently, it becomes much harder to identify fraud that develops across multiple channels or over time.

Worth knowing:

Acoru replaces fragmented fraud detection by unifying data across:

• Online banking
• Mobile banking
• Payment systems
• Contact centers
• Existing fraud solutions

By bringing together signals from multiple channels, systems, and data sources into a single account-centric risk view, Acoru enables you to identify patterns and connections that siloed solutions are more likely to miss.

4. Real-Time Payments Leave Less Time to React

With real-time payment systems, money can move in seconds, leaving very little time to detect and stop fraud. This is especially challenging in scams like APP fraud, where customers authorize payments themselves after being manipulated by fraudsters.

By the time anyone realizes something is wrong, recovering the money becomes extremely difficult or impossible.

Worth knowing:

Unlike solutions that evaluate risk at the moment a payment is initiated, Acoru continuously monitors account behavior and risk over time.

By identifying gradual changes in account activity long before a payment is made, it gives you time to investigate suspicious behavior and intervene before a fraudulent payment is authorized.

Common Red Flags for Payment Fraud and How to Spot Them Early

Payment fraud rarely happens without warning. Here are some of the most common red flags and the strategies you can use to identify them:

1. Sudden Change in Payment Patterns

A sudden shift in how a customer makes payments can be an early sign of fraud. For example, someone who normally uses their account just for bills may unexpectedly send a large international wire or make several unusually high-value payments.

While these changes may have legitimate explanations, they should be reviewed when they don't align with the customer's typical activities.

How to spot it early:

• Monitor for gradual or sudden changes in payment size, frequency, destination, or currency compared to the customer's normal routine.
• Compare new payments with the customer's historical patterns rather than evaluating each transaction in isolation.
• Look for unusual payment activity that coincides with other changes, such as a new device, updated contact information, or recently added beneficiaries.

Worth knowing:

Acoru's AI Workforce continuously monitors account activity for signs of developing fraud. When it identifies potential fraud-preparation indicators, it can automatically investigate the event and initiate approved mitigation actions, all in accordance with your institution's policies and controls.

2. Inactive Account Suddenly Becomes Active

When an inactive account suddenly becomes active, particularly with large transfers, new counterparties, or multiple transactions in a short period, it is a notable change that deserves closer review.

Such activity may indicate an account takeover or the use of the account to move fraudulent funds.

How to spot it early:

• Pay attention to what happens immediately before the account becomes active again, such as profile updates, new devices, password resets, or changes to authentication methods.
• Assess whether the customer resumes their previous activity patterns or immediately begins behaving in an entirely different way, such as receiving large deposits or sending high-value payments.

3. Account Takeover Signs in Customer Profile Changes

Fraudsters often change customer information after gaining access to an account to maintain control. Updates to phone numbers, email addresses, devices, or authentication methods may be legitimate on their own, but multiple changes within a short period can indicate account takeover.

How to spot it early:

• Look for multiple profile changes occurring together instead of evaluating each update in isolation.
• Monitor the sequence of account events, such as contact detail changes, authentication updates, new device registrations, and subsequent payment activity.

If multiple account changes occur together, assign a higher risk score and trigger additional review or verification steps.

4. Multiple Small Test Transactions

Fraudsters often make a series of small transactions before attempting a larger payment. Sometimes these transactions are used to test whether a stolen card or compromised account is active.

In other cases, fraudsters split a larger amount into multiple smaller transactions to avoid reporting thresholds and detection rules.

These low-value transactions may appear harmless on their own, but together they can be an early indicator of fraud preparation.

How to spot it early:

• Monitor for clusters of low-value transactions made within a short period rather than evaluating each payment individually.
• Look for patterns such as repeated low-value payments, gradually increasing amounts, or several failed payment attempts before a successful transaction.
• Check whether the same activity is occurring across multiple cards, accounts, or devices, as this may indicate coordinated fraud.

5. Payments to Suspicious Recipient Accounts

Sending money to an account that shows signs of money mule activity is a key red flag for payment fraud. Although fraud monitoring often focuses on the sender, the receiving account may already have a history of suspicious behavior.

Considering both sides of the transaction helps identify risky payments that might otherwise appear legitimate.

How to spot it early:

• Monitor accounts that receive payments from many unrelated sources and quickly transfer the funds elsewhere.
• Analyze relationships among customers, counterparties, accounts, and devices to identify hidden mule networks.
• Extend monitoring beyond your own customers by incorporating counterparty and cross-institution intelligence to identify risky payment destinations earlier.

Worth knowing:

Acoru's Consortium Manager helps financial institutions identify suspicious recipient accounts beyond their own customer base through a real-time, privacy-preserving intelligence network. Rather than relying on static blacklists, it continuously shares actionable fraud intelligence without exposing customer data.

It provides visibility across:

1. Your own accounts

2. External accounts your customers transact with

3. External accounts with no direct relationship to your institution through optional consortium sharing

This helps financial institutions:

• Identify suspicious recipient accounts and emerging mule networks
• Receive real-time account risk insights and dynamic account classifications
• Securely share fraud intelligence using privacy-enhancing technologies, including zero-knowledge proofs
• Integrate consortium intelligence with existing fraud detection systems

6. Signs That a Customer Is Being Coached

In APP fraud, victims are often coached in real time over the phone or through messaging apps while logging in, adding a new payee, or approving a payment.

This pressure can change how they interact with their device, creating early behavioral signals before the payment is completed.

How to spot it early:

• Look for unusually long banking sessions before a payment, especially when the customer was recently active on a phone or messaging app.
• Detect signs of stress or external guidance, such as long pauses between actions, erratic mouse movements, unusual touch patterns, or excessive device movement.

Strengthen Your Payment Fraud Detection with Acoru

Many payment fraud schemes don't begin with the fraudulent payment. Risk often develops gradually through changes in customer behavior, account activity, and counterparties, along with other warning signs that appear before a suspicious payment is made.

Acoru helps you identify payment fraud earlier by showing how risk develops over time.

Rather than analyzing only individual transactions, it continuously evaluates account behavior, pre-fraud signals, and counterparty activity to help you detect scams, money mule activity, and money-laundering patterns before they lead to significant losses.

With Acoru, you can:

• Unify data without replacing existing systems: Acoru brings together data from payment platforms, digital banking channels, customer interactions, and your existing fraud solutions into a single account-centric view. This helps your team see connections and patterns that are often missed when data is spread across multiple systems.
• Assess both the sender and the recipient: Acoru evaluates recipient accounts alongside sender activity, giving you additional context before a payment is approved. By combining counterparty intelligence with continuously updated account classifications, it helps identify potentially risky payment destinations.
• Extend visibility beyond your own institution: With privacy-preserving consortium intelligence, Acoru helps you identify emerging mule accounts, high-risk counterparties, and new fraud patterns that may not yet be visible within your own customer base.
• Improve investigation efficiency with AI: Acoru supports investigators with AI-assisted investigations, automated workflows, and clear explanations that help prioritize the highest-risk cases. This reduces manual effort and enables your team to investigate suspicious activity and respond to potential fraud more quickly.

Request a demo today and see how Acoru helps you uncover the hidden patterns and pre-fraud signals that traditional fraud detection systems often miss.

See Acoru in Action

Fraud in Latin America is moving fast, and the institutions keeping pace are the ones rethinking how they detect, share, and act on intelligence. If the challenges discussed in this episode sound familiar, we would be glad to show you how Acoru works in practice. 

FAQ:

1. How do money mule accounts enable payment fraud?

Money mule accounts are often used to receive and move stolen funds on behalf of fraudsters, making the money harder to trace and recover. Some mule account holders knowingly participate in criminal activity, while others are recruited through fake job offers or online scams, unaware they're helping launder stolen money.

2. Why is APP fraud so difficult to prevent?

Preventing APP fraud is challenging because customers authorize payments themselves after being manipulated by a scammer. Because the customer initiates the payment, there are often no stolen credentials or clear signs that the account has been compromised, making behavioral signals and scam indicators essential for early detection.

3. How can banks detect payment fraud early?

Banks can detect payment fraud earlier by monitoring how account risk develops over time rather than evaluating individual transactions in isolation. Connecting signals across payment behavior, customer profile changes, device activity, counterparty intelligence, and multiple banking channels gives fraud teams the full context needed to identify suspicious patterns early.