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 detectionSo, 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.
Recommended reading
- Verified by Visa: How Much Protection Does It Really Offer?
- The Top 10 Fraud Detection Tools You Need to Have in 2024
- ECI Indicators: How to Understand 3DS Response Codes
- What are Velocity Checks? How Do They Stop Fraud Attacks?
- Proxy Piercing: How Merchants Can Use it to Prevent Fraud
- Card Verification Values: What Are CVVs & How Do They Work?
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.
[noun]/be • hāv • ior • al • frôd • dē • tec • shun/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.
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:
Understanding Normative Patterns
Behavioral analysis allows you to understand the normal patterns of user behavior. This understanding creates a baseline that can be used to identify abnormal patterns indicative of fraudulent activities.
Identifying Fraud Early
If a customer's behavior suddenly changes, this can be an early warning sign of fraud. For example, if a customer who usually makes small purchases suddenly starts making several large purchases in quick succession. This could indicate that their account has been compromised.
Reducing False Positives
A sophisticated behavioral analysis system can help to reduce false positives in fraud detection. If a system only looks at superficial characteristics (like the size of a transaction), it might flag a lot of transactions that aren't actually fraudulent. You can create more accurate fraud detection models by considering more detailed behavioral information.
Adapting to Evolving Tactics
Fraudsters are constantly coming up with new tactics to commit fraud. Behavioral analysis can help you stay one step ahead by continuously learning from new data. This makes it harder for fraudsters to find tactics that the system can't detect.
Personalizing Security Measures
By understanding individual behaviors, you can tailor your security measures to each customer. This can help to prevent fraud while also providing a better customer experience.
Compliance & Regulatory Requirements
In some sectors, businesses are required by law to have systems in place for detecting and preventing fraudulent activities. Behavioral analysis can help you to meet these requirements (where applicable).
Protecting Business Reputation
Repeated instances of fraud can harm your business's reputation. You can protect your brand and maintain customer trust by detecting and preventing fraud more effectively.
Costs
Fraudulent activities can result in significant financial losses. By preventing fraud, behavioral analysis can help you to save money in the short term. You can also reduce long-term costs through more effective and accurate projections of future strategies.
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:
False Positives
A customer's buying habits could change for legitimate reasons; for example, a large purchase for a special occasion that is outside the buyer’s usual shopping pattern. This may trigger false alerts and potentially lead to unnecessary transaction denials or investigations. This could inconvenience or alienate customers.
Data Privacy Concerns
Behavioral analysis requires collecting and analyzing vast amounts of user data, which can raise privacy concerns. Regulations such as the GDPR in the European Union have strict rules about data collection and use, which businesses must adhere to. Handling this responsibly and transparently is a must.
Complex Implementation
Effective behavioral analysis systems often use complex algorithms and machine learning models, which require expertise to develop and maintain. Smaller merchants may find it difficult or expensive to implement these systems.
Time-Consuming
Behavioral analysis can be time-consuming, depending on the system's complexity and the amount of data. Processing and analyzing large volumes of data can require substantial computational resources.
Adapting to Changes
User behavior can change over time or due to special circumstances (such as a pandemic). These changes can make past behavior a less reliable indicator of fraud, requiring the system to be regularly updated and adapted.
Dependence on Quality of Data
The effectiveness of behavioral analysis in detecting fraud depends heavily on the quality and comprehensiveness of the data. If there are gaps in the data, or it's not accurately captured, the analysis may not be effective.
Difficulty in Detecting New Fraud Tactics
Behavioral analysis systems may initially struggle to detect novel threats as fraudsters continuously adapt and develop new tactics. Without pre-existing data on these new behaviors, the system might not recognize them as fraudulent.
Potential Bias in Data
There's a risk that the data used to train the behavioral analysis system may unintentionally introduce bias. For instance, if the model is trained mostly on data from a certain demographic or region, it may not perform as well when analyzing behaviors from a different demographic or region. This could lead to missed instances of fraud, plus a higher rate of false positives within the underrepresented groups. Ensuring unbiased, representative data sets for model training can be a significant challenge.
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.
FAQs
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.
