Link AnalysisHere’s How Graph Networks Can Help You Uncover Fraud Patterns

How Does Link Analysis Work? Here’s What You Need to Know.

Fraud rarely happens in a vacuum.

On one end of the spectrum are fraud rings, which are essentially organized gangs of criminals who collaborate closely to carry out wire fraud, mail fraud, payment card fraud, and other financial crimes. On the other end are the “lone wolves,” who carry out fraud attacks on their own.

But, even scammers who act alone don’t truly do everything by themselves. For example, they may learn and share fraud tactics on the internet, buy compromised logins or card numbers on the dark web, or recruit unwitting accomplices using social media.

Even the most diligent and privacy-minded scammers are going to leave behind some kind of trail. When they do, link analysis might help expose their activity.

What is Link Analysis?

Link Analysis

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Link analysis is a forensic technique that examines the connections, relationships, and dependencies between individuals, accounts, addresses, devices, IP addresses, payment cards, transactions, and other entities or events for possible signs of fraudulent behavior.

Essentially, link analysis provides context to fraud. Rather than examining a single device or individual’s involvement in fraudulent activity, link analysis allows you to look at relationships between actors for evidence of overlap.

Link analysis can be performed using graph networks, a type of database that lets you visualize connections (represented via edges) between entities (represented via nodes). This way, you can see how the instances of first-, second-, or third-party fraud you encounter are related to each other.

How Graph Networks Detect Fraud

What Data Points are Used in Link Analysis?

Link analysis uses a combination of transaction data, account activity and usage data, and personally identifying information (PII) to pinpoint the entities involved in fraud and how they’re related to each other.

For instance, a scammer who creates a user account can leave behind a goldmine of data, including:

• Name
• Email addresses
• Phone numbers
• Payment method information (if left on file)
• Billing/shipping addresses

Usage logs, which are automatically generated whenever a customer interacts with your online storefront, can also be harnessed for useful data, such as:

• Login times and attempts
• Checkout times and attempts
• Password changes
• Browsers used
• Touchscreen gestures
• Webpage dwell times
• IP addresses
• Geolocation information

Transaction-level data can also help pinpoint scammers and uncover hidden relationships between attacks. This includes data like:

• Items purchased
• Checkout method
• Payment methods used
• Order value
• Repeat order status

These complementary data points, when examined in concert with each other, can help you gain a fuller picture of when, how, and who is behind the fraud that you’re experiencing. More importantly, it can help you understand whether these attacks are one-offs, or whether they’re related.

Examples of How Link Analysis Can Stop Common Fraud Tactics

So far, we’ve covered how link analysis works, including the type of information you can link together. But, how exactly can it help you root out fraud in practice?

Below, I’ve taken a few of the most common fraud tactics and shown how link analysis can help you fight them off:

The insights you gain from link analysis should ultimately help you develop more nuanced fraud prevention rules, which you can either implement manually or use to fine-tune AI-based anomaly detection algorithms.

Regardless of how you choose to roll out these enhanced rules, link analysis should enable you to pivot away from static measures towards multi-dimensional and relationship-based approaches. This will be key to thwart sophisticated attacks, drive down false negatives, and identify possible connections between bad actors.

Challenges & Other Considerations

For all its benefits, link analysis does come with a few downsides that are worth mentioning and mitigating:

Link analysis can help reveal your interconnected fraud risks. But, mining disparate data points for insights may also brush up against guardrails established by global data privacy regulations, including the GPDR, CCPA, and others.

To be clear, collecting data isn't the problem, at least if you do so ethically. Rather, it's the inferences you draw from linked data that demand careful governance. Most notably is the fact that, even if data points like device IDs and transaction histories are pseudonymous in isolation, they can be inadvertently re-identifying in aggregate.

Because you risk crossing privacy thresholds, you’ll need to be judicious about collecting, linking, and analyzing data. Be sure to explicitly disclose how your customers’ data will be used, and obtain consent for both collection and subsequent analysis.

Some accounts may share similarities even though they are actually unconnected with each other. Shared IP addresses — which can result when users connect to public Wi-Fi or cellular networks — can create apparent but ultimately spurious links. The same applies to family members using the same device, or even just sharing the same last name.

If your approach to link analysis is overly aggressive, you could risk generating false positives. You could end up flagging honest buyers and create considerable friction for your customers, the vast majority of whom are legitimate.

This is a difficult balancing act: your linking algorithm must be sensitive enough to genuinely related threats, yet not so trigger-happy that it mischaracterizes uncorrelated attacks as related to one another. Fine-tuning your link analysis strategy must therefore be an iterative process that involves continuous monitoring and adjustment based on real-world outcomes, rather than merely theoretical connections.

Link analysis, like all forms of analysis, inherently relies on past data to predict future trends. But, the future is often very different from the past, especially in the context of security risks.

That’s because fraudsters are constantly adapting their techniques to evade detection, which means that tomorrow’s threats will very likely be more complex than today’s. This creates a latency issue: link analysis can’t identify a new threat, known as a zero-day vulnerability, until it already does significant harm to your business and establishes a discernable pattern within your network.

Link Analysis Best Practices for eCommerce Merchants

For link analysis to live up to its role as a mechanism for detecting and defeating fraud, you’ll need to follow a strategic approach to data integration, usage frequency, and human oversight. To start, I’d recommend that you:

#1  |  Centralize Data for Full Visibility

Link analysis harvests insights from disparate data points generated across the entire customer journey, not just within isolated transaction or account silos. To do this effectively, all of your data must live in one place.

Break down barriers between platforms by funneling desktop and mobile activity logs, CRM data, customer support interactions, loyalty program activity, and even marketing engagement data into a single repository. Doing so gives you a unified view of your data and can help you conduct link analysis with as much context as possible.

#2  |  Use Link Analysis Regularly

Link analysis can help you investigate fraud incidents after they occur, but you should also use it regularly and proactively. Integrate link analysis into routine workflows across the buyer journey. I’m talking during new account creation, before approving high-value transactions, and when reviewing flagged orders.

Treating it as a continuous and always-on monitoring tool can help you spot emerging anomalies or subtle shifts in fraud tactics well before they inflict substantial damage.

#3  |  Combine Link Analysis With Human Expertise

Link analysis algorithms are adept at identifying patterns and connections, but they lack real-world intuition and have no context beyond the data you supply.

That’s why you’ll need to keep human fraud experts in the loop, who can help you validate the significance of identified links, interpret ambiguous results, or even override automated decisions.

#4  |  Train Your Fraud Teams

Not all link analysis tools produce easily intelligible results, so you may need to train your fraud team to understand its outputs.

To be clear, training must extend beyond basic software operation and should instead detail how to think critically about relationships represented via graph network data. Analysts must understand how to interpret visualizations, recognize fraud structures within networks, spot system limitations like over-linking, and critically evaluate the strength and meaning of connections for accurate risk assessment.

Just One Tool in the Kit

Link analysis is a powerful fraud detection technique that can help you pinpoint the users, devices, payment methods, and inventory items involved in friendly fraud chargebacks.

The problem, however, is that knowledge is only half the battle. Knowing how to thwart invalid chargebacks — and not just where they come from — is just as important of a priority.

At Chargebacks911®, our dual-layered, end-to-end chargeback management solution can help you detect, prevent, and fight invalid chargebacks.

Curious to learn more? Contact us for a no-obligation ROI analysis today.