Banks and other large financial institutions have provided a juicy target for criminals since their inception. But not all bank robberies begin with a note slid across a teller’s counter or by a masked cowboy with a 6-shooter. 

One of the most famous incidents of banking fraud was also one of the first. The 1873 Bank of England Forgeries introduced it to the world. The newspapers at the time called it one of the “most skillful attempts to prey upon the complex organization of modern commerce.” The specific variety of crimes committed by those fraudsters continues today, with improvements due to technology. Fortunately, there are also modern ways to fight them with banking fraud prevention solutions. Here are five varieties of banking fraud and methods of prevention:

1. The Crime: Wire Fraud

While the specific method used by the criminals in 1873 would be much harder to pull off (they forged physical banknotes), banks still contend with the essence of their crime. They make the bank think that large sums of money are coming from a reputable source when they are completely fake. These days, fraudsters commit this crime electronically, in the form of wire fraud. 

The Solution: Artificial Intelligence

With so many transactions occurring simultaneously, banks cannot look at each individual transaction and verify that each transfer of money is legitimate. This would create enormous bottlenecks, destroying modern life’s rapid pace and customer expectations for online banking. Instead, banks should use automated systems programmed to recognize behaviors specific to fraudulent activity. These AIs continuously scan the data passing through a system, flag suspicious transactions, and alert humans to look closely. 

2. The Crime: Credential Stealing

It’s a lot easier to pretend to be somebody else when you have the entire internet between you and your target. While doing this, fraudsters use many methods to acquire data that uniquely identifies their users. Data possibilities include social security number, driver’s license number, or birth date.

Most commonly, fraudsters employ phishing, in which the criminal poses as a legitimate business (like a bank or retail store) to trick the victim into giving their credentials. Often, these credentials include passwords but can include more dangerous information, such as a customer’s password reset verification answers. For example, this might be their mother’s maiden name or first pet’s name. 

The Solution: Biometric Data

A strong password is better than a weak password, which is better than no password at all. The strongest passwords deter fraud excellently, but they won’t help much if the criminal convinces a user to share them willingly. Multi-factor authentication acts as an additional layer and mitigates some of the fraud that occurs when passwords are compromised. 

Knowledge and preparation best protects users from phishing, but biometric data proves useful, too. Biometrics are the telltale traits of a user that are difficult to duplicate like the cadence of their voice. They add an additional layer of safety from a fraudster pretending to be a legitimate user, aiding in banking fraud prevention.

3. The Crime: Account Takeover

An account takeover is similar to credential stealing, but with more bad news. In the best-case scenario, lost credentials lead to a bank account password reset for the victim. The worst-case scenario for account takeover is plain old identity theft, which puts the whole organization at risk. 

This kind of fraud is more common in the post-COVID landscape. As we’ve moved many in-person services we use online, we’ve found that switching to cloud-based solutions enables more connectivity than ever before. Unfortunately, it also makes us more vulnerable to electronic fraud.

The Solution: Consortium Data

The value of a system designed to detect fraud increases as the quality and quantity of the data it accesses increases. Using data pulled from a wide variety of sources increases the diversity of the information, multiplying security and analysis. This data is called consortium data, as it comprises collective intelligence from multiple sources within the same industry or sector.

When banks work together and share their data on fraud that has been perpetrated against them, they create a database of known threats. This gives the automated systems more examples to measure possible fraud against. When it thinks an account might have been taken over, it looks for specific behavioral cues. It then alerts the account managers to look closer and take action if necessary.

4. The Crime: Money Laundering

As long as people have been stealing money, or acquiring it illegally, they’ve looked for ways to make the ill-gotten gains look legitimate. “Dirty” money comes from fraudulent activities and crimes of many varieties. Whatever the illegal activities are, they usually involve gathering currency – stealing money – from multiple possible sources. 

No matter where the money comes from, laundering it “cleans” the money by passing it through legitimate channels. Fraudsters target banks for making dirty money clean because passing currency from one account to another is one of the things banks do extremely well. Not only is acquiring dirty money illegal – so, too, is laundering it. Banks that don’t take steps to prevent money laundering take a big risk and face legal repercussions.

The Solution: High Tech Standardization

It seems simple, but the security of a business’s data increases dramatically when every aspect of the business operates on the same system. This rings particularly true for financial institutions, as many legacy systems create big holes that fraudsters can exploit. 

Legacy systems aren’t just old software (though they often are) – they also include physical ledgers and paper records. The sooner these old systems incorporate into a single solution, the better for everyone.

5. The Crime: Accounting Fraud

Accounting fraud primarily affects business lending. Businesses commit accounting fraud by falsifying data about themselves in order to appear more than they actually are. Based on fraudulent bank statements, banks grant loans to these businesses. As a result, these phantom businesses never repay the loans, nor do they intend to, leaving the bank with the balance.

Coincidentally, this is what the 1873 Bank of England perpetrators did, with a fake train car manufacturing company.

The Solution: Machine Learning

Automation of data gathering and analysis has revolutionized the world of banking. With greater and greater frequency, companies are learning that simple rule-based AIs just aren’t cutting it in banking fraud prevention. In comes machine learning, cybersecurity that updates itself to protect against new threats, a solution offers as part of our product suite.’s solution features machine learning that focuses squarely on anomaly detection, identifying the traps before they spring. By also factoring in the location of the login, deep learning models detect the sometimes-subtle patterns of fraud and use behavioral analysis to flag high-risk sessions. In the world of banking fraud prevention, this is especially useful. Banking institutions do not have to constantly update or change their security systems. Rather, they can employ a self-regulating and self-improving system like’s tools powered by machine learning for banking fraud prevention. 

This, combined with entity analysis, can stop criminal collusion with other fraudsters and the security threats they pose in their tracks. By analyzing relationships between bad data and criminals committing fraud, security providers can differentiate between a single fraud attempt and an organized attack. offers lots of effective ways to protect your bank from fraudulent activity. Contact our experts today to find out how we can help you create the perfect defense.