Banking fraud has evolved far beyond simple card theft, with criminals using a wide range of advanced tactics, from manipulating people into willingly sending them money and using AI to make scams more convincing to stealing identities and gaining access to victims' bank accounts.
According to UK Finance, financial fraud cost the UK £1.28 billion in 2025, while the Federal Trade Commission reported that US consumers lost more than $12.5 billion to fraud in 2024, a 25% increase from the previous year.
In this guide, we'll explore the different types of banking fraud and prevention methods so you can better understand how these threats emerge and what you can do to counter them.
Many of the earliest warning signs appear during onboarding, login, account changes, beneficiary creation, and shifts in customer behavior. Monitoring these events together helps financial institutions detect fraud before money leaves the account.
Unauthorized fraud is best stopped through strong authentication and access controls, while authorized push payment (APP) scams require detecting customer manipulation, unusual behavior, and risky beneficiaries before a payment is approved.
Looking at individual transactions or sessions in isolation is no longer enough. Combining account interactions, counterparty relationships, and signals across all channels provides the context needed to identify emerging fraud earlier and reduce false positives.
Acoru continuously assesses account risk, builds a unified view of all of your channel and fraud prevention data sources, evaluates recipient and counterparty risk, and enables privacy-preserving fraud intelligence sharing. This allows institutions to detect threats earlier, stop both authorized and unauthorized scams, and strengthen fraud prevention across the entire customer journey.
Banking fraud is the intentional use of deception, stolen information, or compromised accounts to steal money or gain unauthorized access to financial services for the purpose of misusing them. It can target retail customers, businesses, financial institutions, and the broader payments ecosystem.
Banking fraud generally falls into two broad categories:
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Unauthorized fraud |
Authorized fraud |
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Who initiates the payment |
A fraudster acting without the customer's knowledge or permission |
The genuine customer, after being deceived or manipulated |
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Common examples |
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|
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Customer intent |
The customer never intended to make the payment |
The customer intends to make the payment but is deceived about the recipient or purpose |
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Strongest signals |
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Main prevention focus |
Prevent unauthorized account access and fraudulent transactions |
Detect customer manipulation and scam indicators before the payment is initiated |
This distinction has important implications for fraud prevention. Unauthorized fraud often begins with compromised credentials or suspicious access, making authentication controls highly effective.
Authorized push payment scams are different because customers complete the payment themselves after being manipulated. As a result, institutions must detect changes in customer intent and behavior before the payment is authorized rather than relying solely on login or authentication controls.
Below are some of the most common types of banking fraud, when they typically emerge, and the early indicators that may signal developing fraud.
Identity and application fraud occurs when criminals use stolen or synthetic identities to open accounts, obtain credit, or access financial services. The earliest signals usually appear during onboarding, before an account is approved.
Early indicators include:
Account takeover happens when a criminal gains access to a legitimate customer's account using stolen credentials, phishing, malware, SIM swapping, or other methods of compromising account credentials.
The first signs typically emerge during login and the customer's initial account session.
Common indicators include:
Card fraud involves a criminal using a payment card or stolen card details without the cardholder's permission to make purchases, withdraw cash, or carry out other transactions. The earliest signals usually appear during card authorization and account activity.
Early indicators include:
Impersonation scams occur when criminals impersonate a trusted organization, such as a bank, the police, a government agency, or a well-known company, to convince customers to authorize a payment. These scams usually become visible during payment preparation, before the payment is submitted.
Early indicators include:
Invoice fraud involves criminals changing legitimate payment instructions, while CEO fraud involves criminals impersonating senior executives to persuade employees to make urgent payments. The earliest signals typically appear during beneficiary verification and payment preparation.
Early indicators include:
In investment fraud, criminals promise unrealistic returns to convince victims to send money to fake investment opportunities, cloned firms, or fraudulent trading platforms. Scammers often build trust over time, and these scams typically involve repeated account activity rather than a single transaction.
Early indicators include:
Romance fraud occurs when a criminal builds a relationship with a victim before asking for money, often claiming to need help with medical bills, travel expenses, business problems, or other fabricated emergencies.
These scams usually emerge through long-term account activity, as victims often make multiple payments over weeks or months.
Early indicators include:
Many financial institutions still focus their fraud controls on the transaction itself. However, warning signs can emerge at different stages of the customer journey, from account access and payment preparation to the transaction itself.
This is why traditional transaction-based fraud detection is no longer sufficient. Modern fraud prevention requires continuous visibility across the entire customer journey, allowing institutions to identify developing risk before it becomes a confirmed fraud event.
The table below compares the key differences between rules-based transaction monitoring, real-time behavioral monitoring, and continuous account monitoring.
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Maturity stage |
Detection model |
Detection approach |
Typical response |
Main limitation |
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Rules-based monitoring |
Reactive |
Rules applied to individual transactions |
Decline or review the payment |
Delayed intervention and rigid thresholds |
|
Real-time monitoring |
Reactive |
Dynamic scoring across behavioral biometric markers |
Step-up checks, targeted warnings or case review |
May identify suspicious behavior but often lacks the broader context needed to prevent authorized push payment (APP) |
|
Continuous account monitoring |
Predictive |
Account-level intelligence and shared network signals |
Intervene before the payment or restrict the recipient |
Requires strong data governance and collaboration |
Here are some of the best strategies you can use to increase your chances of being one step ahead of banking fraud:
Continuous account monitoring helps you bring all your channel and fraud tool data together into one unified risk view of the account. It identifies how account interactions, counterparty relationships, and risk change over time. Instead of waiting for a suspicious payment, fraud teams can detect developing patterns across connected data sources, including onboarding, login, session activity, account changes, beneficiary creation, and payment preparation.
To support continuous account monitoring, look for solutions that can help you:
Worth knowing:
Acoru continuously tracks pre-fraud signals across the customer journey, including:
It analyzes risk patterns to continuously score and classify accounts, helping you detect fraud during the preparation phase.
Traditional fraud tools often evaluate one event at a time. An access-security system checks a login, another platform assesses a profile change, and a transaction-monitoring engine scores a payment.
Each event may look reasonable independently:
1. The customer logs in from a recognized device
2. They check their balance and move savings into their current account
3. They request a higher payment limit
4. They add a new beneficiary
5. They send a small test payment
6. They return later and transfer a much larger amount
However, in an investment or impersonation scam, this sequence may indicate manipulation, even if the customer passed every authentication check. If the institution only evaluates the final payment, it loses the context needed to understand how the customer’s behavior changed.
Worth knowing:
Acoru works alongside your existing fraud stack rather than replacing it. It collects and combines signals from device intelligence, behavioral analytics, authentication, transaction monitoring, and other fraud systems to create a unified account-level view of risk.
This helps fraud teams connect seemingly unrelated events across channels and identify emerging fraud earlier.
Most fraud controls focus on the customer sending the payment. While this can help identify account takeover or signs that a customer is being manipulated, it only tells part of the story.
The receiving account can provide equally valuable evidence. By monitoring how beneficiary accounts receive, hold, and move money, you can identify risks such as:
Worth knowing:
Acoru extends risk assessment beyond your own customers by analyzing external counterparties and receiving accounts. When a customer enters a recipient account, Acoru instantly provides account-level risk context based on internal account interactions, omnichannel signal patterns, and relationships.
To provide this visibility, Acoru analyses accounts across three levels of coverage:
This layered approach helps uncover potential money mule accounts, connected fraud networks, and other high-risk counterparties that may not be visible through sender-side monitoring alone.
Fraud networks rarely target a single financial institution. The same devices, identities, beneficiary accounts, and criminal networks often appear across multiple banks. Sharing fraud intelligence helps institutions identify risks they could not detect using their own data alone, such as:
Traditional intelligence sharing often relies on static blacklists, such as compromised credentials, suspicious IP addresses, fraudulent phone numbers, and known mule accounts. While these remain useful, they quickly become outdated and provide little behavioral context.
A much more efficient approach is to share continuously updated account-risk intelligence using privacy-preserving technologies that allow you to identify emerging mule networks without exposing customer data.
Worth knowing:
Acoru's Consortium Manager enables financial institutions to collaborate through a privacy-preserving intelligence network. It helps you:
Many of the strongest fraud indicators appear before a payment is initiated. Risk signals spread across channels and prevention tools can be combined to reveal developing fraud across the customer journey, allowing financial institutions to intervene before a suspicious transaction is ever flagged.
Acoru helps financial institutions detect these early indicators by continuously analyzing account activity and interactions, counterparty relationships, and pre-fraud signals across the entire customer journey.
With Acoru, you can:
Request a demo to see how Acoru can help your organization identify fraud earlier, reduce losses, and strengthen fraud prevention across the entire customer lifecycle.
APP scams are harder to prevent because the customer authorizes the payment after being tricked by a fraudster, so the transaction appears legitimate. Banks need to look for signs of manipulation, unusual behavior, and risky beneficiaries before payments are approved.
Transaction monitoring checks each payment individually for signs of fraud, while account-level monitoring examines customer activity over time. This gives financial institutions more context, helping them detect fraud earlier and identify scams that may not be obvious from a single transaction.
No. MFA is highly effective at preventing many forms of unauthorized account access, but it cannot stop scams where customers willingly authorize payments. Preventing these scams requires monitoring account activity, customer interactions, and other early warning signs before a payment is initiated.
Money mule accounts are used to transfer and conceal stolen funds, creating distance between victims and the criminals behind the fraud. This makes it more difficult for financial institutions and law enforcement to trace the money and recover stolen assets.