Learn about the most common types of digital banking fraud in the current threat landscape, effective banking fraud detection techniques, tools, and technologies, best practices and future trends, including AI for fraud detection.
As high-profile guardians of wealth, banks are among the most obvious targets for fraudsters seeking financial gain.
That's why the banking industry faces the toughest fraud detection and prevention challenges imaginable.
The banking sector is charged with fighting fraud at enormous scale. According to a recent study¹, banks earning at least $10 million in annual revenue face an average of 2,000 attempted fraud attacks per month. Larger, high-profile banks can face tens of thousands of fraud attacks per month.
At this scale, a 100% manual (e.g. carried out by human analysts) fraud detection and prevention strategy isn't sustainable.
Banks have no choice but to adopt automated solutions for fraud detection and prevention.
Financial losses for the bank and its customers. The most direct consequence of successful fraud is financial loss (up to 5% of revenue, according to a 2022 study²). While undetected fraud can result in financial losses for customers, fraud that is detected is typically compensated by the bank in order to maintain trust, leading to direct financial losses.
Damage to customer trust and the bank's reputation. Banks that fail to develop an effective fraud detection and prevention strategy can suffer damage to their public reputation and customer trust, negatively impacting the business's overall standing in the market.
Concern over regulatory compliance. Banks are legally obligated to implement robust fraud detection measures to comply with financial regulations and data protection laws.
While the volume of fraud attacks is a serious problem for banks, another challenge is the variable nature of attacks.
Here are the fastest growing types of fraud affecting banks this year:
Despite the ever-evolving tools, tactics and techniques of fraudsters, almost all digital banking fraud (including mobile banking fraud) has something in common: it involves payments or withdrawals from the victim's account that differ from the victim's typical pattern of behaviour.
Some of these differences are so subtle that they can be difficult for a human analyst to detect.
This is why automated real-time fraud detection tools, such as Antifraud, are essential for fraud detection in banking.
Automated banking fraud analytics tools monitor dozens of behaviour (behavioural biometrics) and device (device fingerprinting) signals in real-time, then ship them to connected tools, such as Splunk, for analysis.
The predictable nature of fraud is the foundation of the fraud detection field.
It means that regardless of the evolving tactics of fraudsters, fraud detection techniques that detect anomalies in the typical behaviour or device signals associated with an account will continue to remain effective.
Here's an example of how automated fraud detection often works in the banking setting:
1. A fraudster sends a phishing email to a banking customer.
2. The phishing email is used to install malware in the victim's internet browser, which is then used to steal the victim's online banking credentials the next time they log in to online banking.
3. Next, the fraudster launches an account takeover attack (ATO) using the stolen credentials and attempts to transfer a large sum of money out of the account.
4. In the background, a fraud detection tool like Antifraud is collecting behavioural analytics and device fingerprint data and sending this data to an enterprise log management and fraud analytics tool like Splunk.
5. Using the Antifraud data, Splunk detects an anomaly and alerts an analyst.
6. The analyst freezes the suspicious transaction before it is completed.
In the early days of fraud detection, unexpected changes in geolocation were often the primary means to detect fraud. Since fraudsters are often not in the same country (or even continent) as their victims, checking geolocation against typical patterns seems like a logical approach.
Over time, it has become increasingly common for both fraudsters and F/CaaS software to use a configurable VPN or proxy to disguise connections as originating from an expected geolocation (such as appearing to originate from Australia when attempting fraud against an Australian bank.)
For this reason, checking geolocation alone is no longer an adequate fraud detection strategy. While geolocation data remains a powerful signal, it must also be combined with device and behavioural data to build a unique "fingerprint" for the bank's authorised users.
A robust fraud detection strategy will require the adoption of several varied fraud detection tools and techniques.
Robust real-time fraud detection in banking relies on synergy between automated fraud detection tools and human fraud analysts.
Typically, automated tools will handle fraud detection responsibilities by leveraging behavioural biometrics, device fingerprinting, and transaction monitoring techniques.
Fraud detection data will then be passed to a fraud analysis or log management tool for anomaly detection.
When anomalies are detected these will be run through an automated rule-set, triggering different responses depending on the amount of fraud risk present.
In a highly effective fraud management system in banking, analysts typically initiate a response and make decisions based on comprehensive groundwork laid by automated real-time fraud detection tools.
Let’s dive into the step-by-step details of a typical fraud management system in the banking industry:
If you'd like to improve your behavioural analytics or device fingerprinting capabilities, we suggest using Antifraud to gather dozens of fraud telemetry signals and ship them to your chosen analysis platform.
We can also help you to establish or uplift your fraud analysis workflows based on the experience we've gained optimising workflows at many major banks.
Feel free to reach out to us a for a brief chat about your use case and to understand how we may be able to help.
Whether you're establishing fraud detection workflows at a new bank or FinTech, or improving your existing workflows, we hope this article has helped you gain a better understanding of fraud detection best practices in the banking sector in 2023.