Bank Fraud Detection in 2024: How Detection Works & Future Challenges

Banks and other financial institutions face the difficult challenge of fraud detection in banking. It’s a constant battle because the methods that criminals use are always changing. However, 96% of banking customers said security and fraud protection are important to them. This tells us that banks can win customers over with strong fraud detection in banking strategies.

But criminals aren’t the only source of fraud. A recent report shared that 65% to 70% of banking fraud happens from employees. It’s clear that this is a multifaceted issue.

How Bank Fraud Happens

Phishing is one common tactic that criminals use. It’s a type of social engineering that involves criminals sending fraudulent messages disguised as real banks. For example, customers might receive a text or email that looks like it’s from your bank. However, these are actually phishing scams designed to trick them into giving you their personal details or online banking login. It’s alarmingly common too.

It might also be incredibly convincing. A 2021 report from Deloitte found that 91% of cyberattacks start this way. Unfortunately, it can also lead to other forms of fraud too. Criminals might use stolen information from phishing attacks to carry out account takeover attacks, where they access customer accounts and transfer funds without their permission. Application fraud is another concern where a scammer might apply for loans or lines of credit using someone else’s information.

Account Takeovers

An Aberdeen Group study found that a whopping 84% of Fintech businesses have experienced an account takeover attack. These can cost up to 8.3% in revenue per year. So it’s no surprise that banks invest in powerful AI systems to help them fight back.

Here are common tactics fraudsters use to commit account takeover fraud:

• Malware
• Credential Stuffing
• Phishing Attacks
• Smishing/Robotexts

Credential stuffing is when a scammer uses stolen credentials to access different online accounts. A hacker might use usernames and passwords leaked from one site to break into your bank account. Smishing involves using SMS to impersonate institutions like banks. Smishing messages might include links or phone numbers that lead to fraudulent websites designed to steal your information. This tactic is rapidly becoming more common, making robust fraud detection in banking systems crucial.

Money Laundering

The American Banking Journal found that every dollar lost to fraud costs banks about $4. But that number does not consider the damage it can do to the bank’s reputation. When you think of financial criminals, money laundering is usually what comes to mind. But money laundering impacts fraud detection too. Since criminals need a way to clean their dirty money, they try to funnel the funds through a bank. It’s part of what makes this such a difficult problem for banks.

How Fraud Detection in Banking Works

Data mining is one way that financial institutions spot and stop fraud. There are different methods you’ll want to know, such as neural networks and pattern recognition. Data mining techniques and tools play a significant role in banking security.

Of course, we can’t forget about machine learning to prevent fraud. According to an article in The Fintech Times, machine learning is vital to stopping fraud in our digital economy. Learning algorithms that are trained on past fraudulent activity help systems to spot anomalies and fraud patterns and respond in real time. Transaction monitoring is also crucial. By tracking regular spending habits, banking AI can flag unusual purchases. It analyzes typical transaction frequency, spending amount, merchant types and even geographic location. And of course, if a customer normally makes transactions in America, but suddenly make one from Eastern Europe, that’s going to get flagged.

The combination of all these technologies allows fraud detection software to become smarter and more efficient over time. This gives human analysts a better chance of responding to actual fraud, rather than just a false positive. They get fewer distractions because AI eliminates the bulk of harmless transactions.

An Example of a Banking Fraud Management System

Real-time fraud detection often requires the cooperation of automated detection tools and people who work as fraud analysts. Here’s an example of what a real-time banking fraud management system might look like.

The Trouble with Geolocation

At one point, banks primarily used geolocation to flag fraud. The logic is simple. Criminals usually don’t commit fraud in the same area that the victim is located. This method worked for a while, but today’s VPNs can fake location. A report from Banking Exchange found that the Neo and Challenger banking sectors will hit over $395 billion by the year 2026. This tells us more people than ever are using online banks, so strong security measures are more important now than ever.

A savvy criminal will only need to configure a VPN to change their location. VPNs give users more anonymity because it creates a direct connection with the website or server and also masks their IP address and routes data traffic through a different server location. This tricks banking AI because it appears as though you are in the same city. Fraudsters know this trick. They are constantly finding ways to stay ahead of banking institutions.

There’s another concern with this method too. The growing availability of fraud-as-a-service means cybercrime is more organized now. The market now includes easily used fraud tools that let inexperienced scammers run advanced scams like credential stuffing. In 2022, Forbes found that Paypal admitted to having 4.5 million illegitimate accounts on its platform. With millions of compromised accounts to steal information from, you can imagine how difficult this problem is becoming for banks and financial institutions.

So while geolocation is a strong factor, it isn’t enough by itself. You also need both device and behavior data to create unique customer fingerprints. Combining the signals from these various techniques gives your system a much stronger chance of catching criminals before they steal funds from your customers. A lot is at stake. Banks have to deal with financial loss, reputation damage, and the stress of meeting legal regulations.

Data Mining Techniques & Tools in Banking

One common technique in fraud detection in banking involves the use of “rules”. Basically, AI uses this pre-programmed set of “if-then” statements to flag any events or actions that violate predefined thresholds. But this method requires a lot of data and precise programming. Here are some other popular tools banks are implementing:

• Statistical Data Analysis Methods: Think probability, parameters, modeling, and regression analysis.
• Artificial Intelligence: AI systems that use machine learning, data mining, and neural networks.
• Behavior and Device Analysis: Powerful systems that leverage behavior biometrics and device fingerprinting to create unique customer profiles.

In order to be effective though, transaction monitoring has to follow certain laws. These laws include knowing your customer (KYC) standards as well as anti-money laundering regulations (AML). It’s incredibly important that banks follow them too. Transaction monitoring works by banks creating a behavioral baseline that outlines what the customer’s usual behavior looks like.

What To Look for in Fraud Detection Tools

With a growing demand, lots of fraud detection software options are now available. But for your own security, there are several things to keep in mind. You’ll want to select options that give you a strong data integration system because this helps you combine various information from a number of reliable data providers, including sanction lists and politically exposed person lists. Another important aspect of these programs involves having access to a robust machine-learning algorithm.

For a quick set up and better value, select fraud detection software that works out of the box. Additionally, powerful AI that offers deep analysis like identity clustering, dynamic thresholds, and graph networks can give you a huge edge against crafty fraudsters. This can include real time fraud detection tools in banking. Having a variety of techniques at your disposal can significantly reduce the chance that a scammer will exploit any gaps in your system’s ability to catch them.

For compliance teams to accurately explain how each case is handled to auditors, your fraud detection tools should make AI analysis easy to interpret. It is just as important that the AI understands what is going on as it is for your employees and any government agency who might need an explanation.

FAQs About Fraud Detection in Banking

What is an example of fraud detection?

A strong example of real time fraud detection in banking would be flagging a customer who normally accesses their online account from their home wifi, but they suddenly access it from a suspicious VPN. A less extreme case could include a customer buying an airline ticket that they immediately attempt to charge back and then claims they never purchased it.

What do banks look for when investigating fraud?

Banks look for a few specific things. This might include analyzing recent transactions or account activity for anomalies. Things like a rapid increase in ATM withdrawal amount and frequency are often big giveaways. When someone creates new accounts using fake documentation, this often signals a bigger scheme that warrants investigating. Banks might also use phone or email communication records to trace any unusual contact and then investigate deeper.

What are rules in fraud detection?

The “rules” in fraud detection in banking are usually programmed into banking systems as if-then statements that use certain data to determine whether to allow or block an action. For instance, if a user is suspected of making a fraudulent purchase with a credit card, the system might instantly block the purchase or require an additional layer of verification from the user.

Conclusion

Fraud detection in banking requires more than just powerful tools and software though. Although banking institutions implement systems that spot fraud and money laundering in real time, your customers also need education and tools that keep their information safe. When it comes to bank fraud prevention and detection, it’s wise to provide easy-to-read informational materials and resources that empower them with knowledge of common banking scams like phishing attacks, smishing and synthetic ID scams.

Customer support that encourages account security, strong passwords, enabling two-factor authentication and regular software updates are easy steps to a more robust approach to detect fraud. With criminals using sophisticated and ever-changing techniques to commit crimes and banks responding with innovative technology to stop them, the battlefield of fraud detection in banking is going to keep changing. Although a single solution might not stop this issue from happening entirely, implementing a variety of proven techniques and strategies can help reduce financial losses for both banks and customers.