Modern fraud no longer kicks the door down. What used to manifest in grand gestures, like brute force attacks or high volume of credit card charges using stolen details until one tactic finally sticks, is now all but a whisper in the wind. Small charges, AI-generated documents that pass verification and synthetic identities engineered to blend in.
Fraud networks often span borders and exploit coordinated infrastructure, including devices, IP addresses, mule accounts and synthetic identities, to commit fraud at scale. According to a 2024 report, over 80% of financial fraud now involves synthetic identities or coordinated small-scale attacks, making detection significantly more complex.
Yet many fraud systems still treat each case as a standalone event, and this approach no longer works. What businesses need is a way to find the hidden threads that connect seemingly unrelated actions and pull on them until the entire fraud operation unravels.
What Is Network Risk Scoring in Fraud Detection?
Network risk scoring is a method of detecting fraud by evaluating not just individual users, but the web of connections around them. Instead of treating each account in isolation, it asks: Who is this user connected to, and what do those connections say about their risk level?
A new customer might seem completely legitimate at first glance. They may provide valid-looking information, pass ID checks and behave like any other legitimate customer does. But if that same customer shares an IP address, phone number, device or email pattern with other accounts previously flagged for fraud (such as chargebacks or stolen identities), their network risk score increases. This connection alone could trigger a review, temporary hold or block before the user ever has a chance to harm the business.
What makes this network risk scoring powerful is its ability to detect fraud proactively, preventing bad actors from even entering the system. Traditional tools tend to focus on individual red flags: a mismatched document, an unusual transaction or a login from an unknown location. But fraud today is rarely isolated. Modern fraud is largely committed by organized fraud rings, involving multiple accounts and shared tools. By looking at how users relate to each other, rather than just their standalone behavior, network risk scoring gives fraud teams a critical edge — one that helps them act before damage is done.
Why Is Network Scoring Important for Fraud Detection?
Fraud networks rely on scale and coordination. They use a mix of synthetic identities, compromised data and reused infrastructure to slip past traditional checks. These tactics are designed to scatter signals and avoid detection by making each account look like a separate, low-risk user.
Network risk scoring cuts through this illusion by focusing on relationships and patterns. It detects when dozens of new customers apply for credit using the same IP address or device ID, even if their personal details all look different. It flags clusters of activity, like multiple accounts sending money to the same destination or logging in from the same location, indicating a shared operator or control point.
This approach also highlights when normal-looking users are involved in abnormal behavior, such as repeated interactions with high-risk accounts or participation in coordinated payment loops. These insights are nearly impossible to surface with isolated signals alone, making network scoring essential for spotting hidden fraud architecture.
How to Identify Fraud Networks Using Link Analysis
Link analysis is the engine that powers network risk scoring. It represents users, devices, emails, IPs and payment methods as interconnected nodes in a dynamic graph. These connections reveal the hidden structure of fraud networks: clusters of accounts linked by shared behaviors or infrastructure.
Instead of chasing individual anomalies, link analysis lets analysts investigate patterns. A graph database maps how accounts relate to each other, allowing teams to follow trails through login locations, payment flows or device usage. For example, if several flagged users access their accounts from the same device or IP, link analysis instantly highlights that overlap.
It also helps pinpoint synthetic identities that, while seemingly distinct, share too many digital traits to be a coincidence. It can also track suspicious money movements, like the same money being sent in circles between accounts or several users cashing out at the same time in a coordinated way.
Graph databases outperform traditional databases when it comes to this kind of work. Their non-linear structure handles multi-dimensional data effortlessly, making it easy to spot complex patterns like collusion or repeat abuse. A fraudster might use a dozen different emails and phone numbers in hopes of staying stealthy, but if all those accounts behave in sync or share the same underlying tech, link analysis surfaces the connection.
This kind of mapping transforms investigations. Instead of reacting to individual alerts, fraud teams can visualize entire networks, identify entry points and take down coordinated actors at scale.
How Different Sectors Use Network Intelligence to Fight Fraud
Network risk scoring and link analysis are transforming fraud detection across industries. Rather than chasing isolated red flags, these tools allow companies to analyze the bigger picture, spotting coordinated activity, shared infrastructure and suspicious behavioral links across user accounts.
- In fintech and digital banking, this means detecting application fraud by revealing when new customers share IPs, devices or phone numbers with known fraudsters. It also helps block synthetic identities during onboarding and flags mule accounts based on transaction patterns that match money laundering networks.
- eCommerce platforms use network risk scoring to expose fake buyer or seller clusters, catch refund abuse rings and detect when multiple accounts share the same payment details or devices (classic signs of account farming).
- In iGaming, it’s about spotting coordinated player collusion, stopping bonus abuse by linked accounts or uncovering bot networks operating at scale.
- Buy-now-pay-later (BNPL) and lending platforms benefit by identifying synthetic applicants grouped by shared traits, assessing risk based on network behavior and preventing repeat fraud by users cycling through alternate identities.
Across all these use cases, the advantage is clear: network scoring brings depth and context. It connects the dots between users and infrastructure, making fraud easier to detect and harder to miss. This not only improves accuracy and automation but also reduces false positives, which is crucial in fast-moving, high-volume environments.
How Network Scores Help Stop Fraud
Network risk scoring breaks down complex relationships into clear, actionable insights. Instead of analyzing users in isolation, it looks at how they relate to each other and scores them accordingly. This allows fraud teams to identify coordinated activity, expose fraud rings and act before losses occur.
- Shared infrastructure detection: Network scoring systems automatically flag accounts that share critical data points like IP addresses, device IDs, email domains or phone numbers. These connections often indicate account farming, mule activity or synthetic identity clusters. By surfacing this shared infrastructure, systems can block or flag coordinated attacks even before a transaction is made.
- Behavioral and data pattern matching: Accounts that exhibit similar behaviors, such as applying for credit at the same time, using nearly identical contact details or mimicking transaction flows, get grouped and analyzed together. This reveals hidden relationships that would be invisible when examining users individually. It’s especially effective against organized fraud rings that try to appear legitimate by adding slight variations to user details.
- Risk sharing with linked entities: Even if a new user appears clean, their network connections matter. If they’re tied to multiple high-risk customers, their score increases, prompting extra checks or automated rejection. This prevents fraudsters from bypassing systems by simply creating fresh accounts.
- Pattern-based anomaly detection: Network scoring detects suspicious aggregate patterns like circular payments, clustered withdrawals or simultaneous activity across accounts. These would go unnoticed in siloed systems but become obvious when viewed as part of a broader network.
Imagine a fraud ring using 50 synthetic accounts, each with unique details but all logging in from the same device. A traditional system might miss this, but network scoring won’t. It connects the dots, elevates risk and enables swift, informed action.
What To Look for in a Network Risk Scoring System
Choosing the right network risk scoring system is key for staying ahead of organized fraud.
- Real-time monitoring: At its core, the system should deliver real-time insights. Fraud moves fast — new connections, behaviors and anomalies emerge constantly and a reliable system must recalculate risk scores instantly as fresh data flows in, enabling proactive intervention instead of delayed reaction.
- Scalability: Modern fraud networks can involve millions of linked entities: users, devices, IPs, payment methods and more. A strong system must handle this volume without performance degradation, allowing teams to assess risk across vast digital ecosystems without missing key patterns.
- Transparent decisioning: Fraud teams need to understand why a user was flagged. That means clear logic, traceable decisions and explainable scores. If a risk score spikes due to shared infrastructure or behavioral overlap, the system should make that cause obvious.
- Flexibility and customization: Every business faces different threats, so a one-size-fits-all model doesn’t cut it. The ability to tune weights, thresholds, and triggers based on sector-specific fraud patterns ensures relevance and accuracy.
- Centralized insights: Most importantly, the system should integrate link analysis and behavioral signals into one unified framework. This means it doesn’t just draw graphs of connections — it overlays them with risk insights like device reputation, velocity anomalies, or known fraud history.
How SEON Helps Uncover Hidden Connections
SEON maps the digital trails fraudsters leave behind, turning them into dynamic risk scores that update instantly as new data arrives. This means you can detect suspicious clusters and coordinated activity the moment it starts.
With customizable scoring rules and insights gathered by digital footprint analysis, SEON surfaces connections others miss. Whether it’s mule accounts, synthetic identities or organized attacks, every signal feeds into a smarter, faster response to stop fraud before it happens.
Free up time to concentrate on running your business, not fighting fraud.
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Frequently Asked Questions
A network score is a numerical value that indicates the level of risk associated with a user or account based on their connections to other entities, such as shared IP addresses, devices, email addresses or links to known fraudsters. This score helps fraud teams prioritize investigations by highlighting accounts that may be part of larger networks.
Network scoring can identify a wide range of fraud schemes, including organized fraud rings, mule account networks, bonus abuse, refund scams and synthetic identity clusters. By analyzing connections and shared infrastructure, it uncovers patterns that traditional single-entity checks often miss.
Graph networks detect fraudsters by visualizing the complex relationships between users, devices, IPs and other data points. This allows analysts to spot clusters of coordinated behavior and shared attributes, making it easier to identify groups of fraudulent accounts acting together.
Network risk assessment helps prevent fraud by proactively identifying accounts that exhibit risky connections or behaviors. Instead of reacting after fraud occurs, it flags suspicious networks early, enabling businesses to block or review high-risk users before damage happens.
Yes, network risk scoring is especially effective at detecting synthetic identity fraud. By examining links between seemingly unrelated accounts, such as shared devices or IPs, it uncovers synthetic identities that pass traditional checks but reveal connections to known fraud activity.
