Banking Fraud Prevention: Everything You Need to Know

Technology can be a double-edged sword. Especially in the banking sector where digital transactions have revolutionized the way we handle our money and assets.

While the online facilities have made our lives easier, they’ve unwittingly opened the doors for cyber crimes, thereby posing serious security threats for banking institutions across the globe.

Gone are the days when bank thieves would rob a branch at gunpoint.

Nowadays, thieves are technically competent and know the intricacies of new-age banking.

Such fraudsters employ a range of techniques to hack into accounts by impersonating customers.

It can be challenging for banking institutions to keep pace with the criminal’s mind-numbing hacking tactics.

However, with the right use of biometrics, algorithms and artificial intelligence, frauds related to banking, credit cards and loans can be prevented to a large extent. 

6 Simple Tips for banking fraud prevention

As per the Financial Crime Report Q2 2021, 83% of frauds attacks have occured online.

What makes financial institutions the prime target of internet fraudsters?

Well, banks serve as a hotbed of online theft as they offer immediate access to funds just at the click of a button.

Hence, it becomes imperative for financial institutions to come up with robust fraud prevention solutions to shield their customers, assets and systems against cyber threats.

SEON is a banking fraud prevention software. You can take a look at the banking fraud prevention tactics listed down by SEON. 

Meanwhile, let us guide you through some of the most effective strategies to avoid banking frauds. 

1. Beware of internal frauds

They say, charity begins at home. As the first important step of combating bank frauds, you need to start screening and auditing your bank’s employees.

According to research conducted by Clari5, banks face revenue losses of around $3.5 trillion globally every year due to employee frauds. 

Moreover, the Association of Certified Fraud Examiners (ACFE), in its 2020 report, has mentioned 2504 cases of occupational fraud from around 125 countries, which resulted in estimated losses of $3.6 bn.

Therefore, monitoring the internal staff becomes a top priority.

Who knows, some of your most efficient and trusted employees might be involved in nefarious activities.

Given the reach and anonymity offered by the dark web, some of your staff might be selling customer account details over there.

A research conducted by Microsoft shows how the majority of cybercriminals try this route before opting for the most complex ones.

It’s easier to gain access to confidential data of their target organizations through employees who fall prey to the lure of money and exploit the internal vulnerabilities of the system that they know inside-out.

Your first line of defense should involve identification and detection of suspicious activity in real time. If not tackled with caution, banking frauds may lead to global financial apocalypse. 

2. Seek assistance of Artificial Intelligence (AI)

Imagine the sheer scale of banking transactions occurring simultaneously on any given day!

With that volume of money transfer happening on a day-to-day basis, it’s impossible to manually oversee each individual transaction and verify whether it’s authentic or not.

It could lead to a massive deadlock-like situation, hampering the pace of operations and denting customer’s expectations and trust.

Modern banks employ AI-backed automated systems that are programmed to track suspicious behavior and detect fraudulent activities.

Using this approach, you can scan the data being processed by the system, raising a concern for unnatural behavior so that your staff can timely intervene and prevent cyber crimes.

For instance, an artificial intelligence powered system can keep track of unusual transactions or spending patterns, triggering an alert to verify customer authenticity.

A customer’s past history of purchases helps make future predictions. Your system is most likely to allow a large overseas purchase to go unchecked if it complies with the customer’s previous purchase records.

While banks are extensively harnessing the power of artificial intelligence, we’ve only begun to scratch the surface as far as exploring the vast capabilities of AI is concerned.   

3. Apply biometrics for secure banking

It doesn’t take much effort to crack passwords, no matter how complicated they may seem to us.

There’s only so many unique characters that you can attempt to add in order to create a password that’s unique and unbreakable.

Since passwords are being repeatedly violated by sophisticated criminals, banks have realized they can’t protect their systems with a random collection of words and characters.

How about using the physical characteristics of a user to add an enhanced layer of security?

After all, a person’s physical traits cannot be duplicated as easily as their passwords.

Biometric data does just that.

It uses telltale features of a user such as facial symmetry, eye color, voice cadence to determine whether the user is impersonating someone or is a legitimate one.

This way, it’s hard to fake a person’s identity, thus preventing banking frauds to a great extent.

It’s advisable to store biometric data within the banking system, not within a user device. Else, if the device is stolen or compromised, biometrics may get tampered with.

Besides voice biometrics, banks rely on behavioral and language biometrics to verify the legitimacy of a user.

4. Make way for multi-factor authentication

Multi-factor authentication (MFA) / Multi-layered security structure has emerged as the backbone of new-age banking institutions.

Banks all over the world have successfully evaded cyber frauds by implementing this hugely popular practice.

Simply put, MFA generates a second level of credentials once the user logs in with their existing credentials.

The second layer of validation may come in the form of one-time password, soft token or hard token. Even if the token is somehow lost or untraceable, the user can generate a new token and easily access their accounts.

Sometimes, password verification is not enough. They may be weak or may get stolen.

MFA technique increases the security of customer data. Mobile number verification is a widely used MFA solution.

A unique, time-sensitive code is sent as a text message to the user’s registered phone number. Once the user enters the correct code, his identity is authenticated.

MFA prevents credential harvesting with its extra layer of data security, reduces frauds, builds trust and helps banks conduct businesses with their customers in a safe and secure manner. 

5. Introduce Machine Learning (ML) to upgrade security

There are instances when cybercriminals have attempted to enter fake information during the customer verification process.

However, what has thwarted those attempts is a robust machine learning algorithm.

Thanks to machine learning, you can build a cybersecurity system that analyzes past behavior, learns from it and updates its rules to tackle new threats.

Simple, rule-based AI has its limitations in dealing with sophisticated fraud mechanisms.

Machine learning, on the other hand, utilizes a self-regulating and self-improving system that minimizes the time and effort invested in constantly updating and modifying security parameters.

Deep learning models are capable of identifying subtle changes in behavior. This will help detect high-risk sessions that may be missing from plain sight.

Statistical analysis is an essential element of banking fraud prevention. Through this technique, you gather as much data as possible, then use it along with algorithms to set up risk patterns.

This is a primary example of blackbox machine learning that customers can easily avail if they want to.

As machine learning prevents users from falsifying data about themselves and their bank statements, it’s hard to dupe banks and extract loans for fraudulent businesses. 

6. Identify fraud patterns with consortium data

In order to design a system that can detect frauds, you should focus on improving the quality and quantity of data accessed by the system.

When data is fetched from a wide variety of channels, the information diversity increases which eventually makes way for a largely secure system.

The more data we pull, the better equipped our banking system will turn out to be.

Consortium data refers to a specific type of data that encapsulates collective intelligence from diverse sources within the same industry and ensures banks can detect critical patterns of the perpetrator.

Such data represents a database of known threats, thus allowing our automated systems to get a clearer picture of what they’re up against.

Banks ought to work in unison and share data related to threats and frauds they’ve encountered or managed to evade.

Putting together this huge bulk of information in an effective manner multiplies your chances of creating a strong backend security system.  

Conclusion