How to Elevate Your Risk Management Strategy With Behavioral Fraud Detection & Analysis
You already understand how important it is to know your customers. But, have you considered the fact that it's equally crucial to understand bad actors who may try to defraud your business?
In our increasingly digital era, fraud detection methods must advance beyond the simple protocol of requesting a username, password, and two-factor authentication for authorization. You must be armed with precise data to discern genuine consumers from deceptive ones. This is the only way to effectively minimize potential losses.Learn more about fraud detection
So, what’s the key? How do you get to “know your scammers” in the same way you know your customers?
What if we told you there was a way to access a veritable fountain of information that can help identify and respond to fraud. Better yet, what if the process of analyzing that data was fully automated? That’s the big idea behind the behavioral fraud detection concept.
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What is Behavioral Fraud Detection?
- Behavioral Fraud Detection
Behavioral fraud detection is a strategy involving an AI application that is used to analyze consumer behavioral patterns in order to detect fraud. This analysis encompasses a plethora of data points, such as usual login times, typical transaction types, and even subtle habits in mouse and keyboard usage.
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Essentially, behavioral analysis is designed to detect anomalies not only at an individual transactional level. These factors have the capability to discern patterns that might escape human notice.
Continued interactions of customers with apps and websites establish a series of patterns. The technology can then craft profiles of “normal” and anticipated behaviors. When it detects practices that are outside these patterns, it can flag them as suspicious.
Behavioral analytics can also be used across an organization. Beyond spotting fraudulent activities from cybercriminals, the system can also be used to identify fraud and unusual behavior within a company's internal systems and staff.
How Does Behavioral Analysis Work in Fraud Detection?
Behavioral analysis in fraud detection hinges on extensive data collection. Contemporary systems in this field can meticulously record and evaluate a range of transaction details. Everything from cursor movements and mouse usage to screen display settings and typing rhythm can be used to construct a general profile for transactions. Additional data can be gathered based on your profile as a merchant. For example, location, brand, typical order value, and even the demographics of their usual customer base can all be factors.
Ultimately, behavioral analysis fraud detection systems are designed to identify various red flags that might suggest fraudulent activity. Signs the system will watch for can include:
Behavioral analysis with machine learning formulates profiles to paint a virtual picture of each customer's typical account activity based on a variety of interrelated factors. Armed with this data, the system can spot deviations when user behavior strays from expected patterns. By constantly monitoring for these and other signs of suspicious behavior, behavioral analysis systems can help to identify and prevent fraud.
Why Is Behavioral Analysis Fraud Detection So Important?
In short: because it works.
According to the Federal Trade Commission data, consumers reported losing nearly $8.8 billion to fraud in 2022; an increase of more than 30% over the previous year. Considering global eCommerce sales are expected to top $58.7 trillion annually by 2028, we can expect fraud to rise in tandem. Indeed, online payment fraud losses are projected to exceed $200 billion over the next five years.
What this data means is that fraud isn’t just here to stay. It’s going to get worse.
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As eCommerce continues to grow and innovate, fraudsters are keeping up by becoming more and more technologically proficient. Fraudsters use increasingly sophisticated tools to spoof devices, locations, and identities. So, we cannot rely on such data alone.
Behavioral analysis, as an automated procedure, can be an invaluable tool. The immense quantity of online transactions renders it virtually impossible for you to monitor incoming data around the clock manually. This software, operating largely independent of human intervention, is capable of identifying and responding to bot and botnet attacks in real time. Moreover, it can do so tirelessly, working all day, every day, without fail.
Benefits of Behavioral Analysis Fraud Detection
Keep in mind there is more to this software than straightforward fraud detection procedures. Using behavioral analytics as a source of customer insight, organizations are able to see more than just historical data about the user.
They can see which pages a user most often visits. They can also see which products users view most often, and which advertisements or promotions lead to higher churn. Naturally, these features can add a tremendous amount of incremental value in assessing customer behavior.
Some of the additional benefits you can expect from behavioral analysis for fraud detection include:
Shortcomings of Behavioral Analysis Fraud Detection
All the factors outlined above make behavioral analysis an invaluable tool in a merchant's arsenal against fraud. But, while behavioral analysis plays a crucial role in fraud detection for all the reasons above, you have to remember: no technology is perfect.
There are a few blind spots that behavioral analysis might miss, or which it simply isn’t programmed to accommodate. Some of the challenges the software might pose can include:
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Despite these challenges, the benefits of behavioral analysis for fraud detection often outweigh the downsides. This is especially true when methods are in place to address and mitigate these shortcomings. It's important to consider this approach carefully, though, and to be aware of these potential issues and plan accordingly.
How to Implement Behavioral Analysis Fraud Detection
As demonstrated, behavioral analysis offers a wealth of new data points to monitor in order to catch suspicious user activity. But, where can merchants get their hands on sufficient data to train a system? And, which providers offer the most benefits?
Well, when searching for a behavioral analysis fraud detection system, keep in mind there are a couple of ways to go about this. You can seek support from:
Whichever method works best depends on several factors that must be determined by the company itself. However, when considering methods and providers, be aware that no fraud detection method can ever be 100% effective on its own. It’s also important to remember that not every act of fraud occurs during the transaction stage.
One Part of a Broader Strategy
While behavioral analysis is extremely effective at detecting red flags, it can’t predict or respond to every threat source. Take first-party chargeback misuse, for example.
So-called “friendly fraud” occurs when a customer disputes a legitimate transaction after the sale has been finalized. It can sometimes take weeks for the dispute notification to show up, which makes traditional fraud tools all but useless in that case.
Considering that friendly fraud represents as much as 70% of all incoming chargebacks, you can see why pre-transactional fraud tools won’t be enough.
Behavioral analysis is just one component of a larger, more comprehensive fraud strategy.
What is a behavioral indicator of fraud?
Any consumer behavior activity that is outside established behavior patterns within a merchant system or eCommerce portal can technically be considered a fraud indicator if it aligns with additional warning signs. For example, if the user displays any red flags such as abnormal location, inaccurate shipping details, or inaccurate card information.
While none of these alone might trigger a decline, the system will factor these details together to determine if the user’s behavior is “normal” or suspicious.
What are the red flags of fraud behavior?
Mismatched card details, unusual transaction values, abnormal transaction volumes, high-risk locations, or use of anonymizing services such as VPNs are all examples of possible fraud.
What are the factors indicating fraud?
As mentioned above, any consumer behavior activity that is outside established behavior patterns within a merchant system or eCommerce portal can technically be considered a fraud indicator if it aligns with additional warning signs. For example, if the user displays any red flags such as abnormal location, inaccurate shipping details, or inaccurate card information.