Fraud is an arms race. Account takeovers, synthetic identities, and organized fraud rings are just a few of the ways attackers are testing organizations’ defenses and looking for weak spots.
Fortunately, there are established fraud detection techniques that enable companies in any industry to fight back. In this article, we’ll explore five of the most important fraud detection techniques that every organization should have in their arsenal.
We’ll discuss everything from identity verification and biometrics to rules-based systems, transaction monitoring, machine learning, and network and link analysis. Each has strengths, limitations, and ideal use cases, but when combined, these methods provide the foundation of a layered approach that makes it far more difficult for fraud to succeed.
Identity Verification and Biometric Authentication
Effective fraud detection starts with ensuring that an individual or organization is who they claim to be. Traditional approaches include document verification and Know Your Customer (KYC) processes. Uploading a driver’s license or passport, cross-checking it against government databases, and verifying basic details are common steps.
Biometrics provide another layer of assurance. Fingerprint and facial recognition are commonly used on smartphones, so customers are accustomed to using these methods for login and verification for certain transactions. Voice authentication is increasingly being used in contact centers.
Behavioral biometrics, such as typing cadence and device interaction patterns (e.g., swipe speed, mouse movements), provide invisible signals that can flag suspicious behavior. Biometric authentication systems continuously run these checks in the background, providing security teams with more information without friction for legitimate users.
Applications and Use Cases
- Banking and Fintech – Customer onboarding, loan applications, remote account opening.
- E-commerce – Reducing account takeover fraud during checkout.
- Healthcare – Patient verification for telemedicine access.
- Travel and Hospitality – Airport biometric gates for faster, more secure boarding.
- Government – National ID programs and e-voting systems.
Benefits
- High accuracy in verifying real identities.
- Strong defense against account takeovers and synthetic identities.
- Faster and easier logins with fingerprints or face scans.
- Biometric traits are harder for fraudsters to fake.
Challenges and Limitations
- Privacy and data protection risks if biometric databases are compromised.
- Potential bias in facial recognition technology.
- High implementation and integration costs.
- Users may resist adoption due to privacy concerns.
Rules-Based Systems
Pre-dating machine learning and other advanced techniques like biometrics, rules-based techniques have long been the traditional approach to fraud detection. Rules-based systems are still commonly used by organizations today, often as a first line of defense, because they’re simple, transparent, and easy to implement.
Rules-based fraud detection applies rules in pre-defined conditions, and takes action based on these rules. For example, a purchase may be declined if it’s over a pre-determined dollar amount. Other common rules-based methods include blacklisting known fraudulent accounts, IPs and devices, or using geolocation to determine whether a login or purchase is originating from an expected location.
Rules-based systems don’t learn. They’re static filters designed to detect obvious suspicious behavior.
Applications and Use Cases
- E-commerce – Blocking transactions above certain dollar thresholds.
- Travel Industry – Preventing booking fraud by blacklisting known stolen credit cards.
- Banking – Denying transactions from sanctioned geographies.
- Retail Loyalty Programs – Automatically flagging multiple accounts tied to the same IP address.
- Corporate IT – Blocking logins outside of business hours.
Benefits
- Transparent and easy to understand.
- Quick to implement with minimal infrastructure.
- Effective for catching well-known fraud patterns.
- Useful as a baseline layer of defense.
Challenges and Limitations
- Static rules can’t adapt to new fraud tactics.
- High maintenance burden to keep rules updated.
- Generates many false positives in dynamic environments.
- Limited effectiveness against sophisticated or evolving fraud.
Transaction Monitoring and Anomaly Detection
Most fraud leaves a trail of breadcrumbs in the form of unusual activity. Transaction monitoring and anomaly detection observe activity in real time and raise alerts on transactions or patterns that don’t match expected behaviors.
Real-time monitoring systems track transactions as they unfold, giving organizations an opportunity to react before losses occur. They build behavioral baselines for each user or account, learning what typical behavior looks like.
Deviations from those baselines trigger alerts, based on risk scores and pre-defined thresholds. For example, these systems may flag activities such as wire transfers to a new country, unusual login locations, or transactions exceeding a certain dollar amount.
Applications and Use Cases
- Payments – Flagging transactions outside normal spending patterns.
- Cryptocurrency Exchanges – Spotting rapid, high-value transfers indicative of money laundering.
- Telecom – Detecting SIM-swap fraud by monitoring unusual account activity.
- Healthcare Billing – Identifying unusual claim submissions by providers.
- Retail – Monitoring gift card transactions for fraud rings.
Benefits
- Real-time detection and response reduces financial impact.
- Builds behavioral baselines unique to each user or account.
- Can be used across many industries, from finance to telecom.
- Can integrate with case management for investigations.
Challenges and Limitations
- High false-positive rates if thresholds are too rigid.
- May frustrate customers if legitimate transactions are blocked.
- Fraudsters adapt quickly to common detection thresholds.
- Requires constant fine-tuning to balance risk vs. customer experience.
Machine Learning and Artificial Intelligence
AI and machine learning are highly effective in fraud detection because fraud rarely follows predictable patterns. Instead of relying on static rules, these systems learn from data, uncover hidden patterns, and adapt to new tactics.
Supervised learning trains models on labeled examples of fraud and legitimate activity to spot similar patterns. Unsupervised learning identifies anomalies and outliers that may indicate emerging fraud schemes without the need for labeling. Advanced techniques like neural networks, deep learning, and natural language processing (NLP) analyze large datasets, detect subtle relationships, and flag suspicious activities.
Together, these techniques provide organizations with broad capabilities to detect fraud from multiple angles. However, it’s important to recognize the challenges and limitations of AI, such as bias in training data, difficulty handling ambiguous or novel scenarios, and a lack of contextual understanding beyond patterns.
AI also cannot make ethical or moral judgments, raising concerns in sensitive applications. Additionally, AI systems often produce false positives or negatives, require large amounts of high-quality data, and struggle with transparency and explainability. These tools should be used to augment the work of human analysts, not replace them. Combining AI’s speed and scale with human judgment and intuition ensures more accurate and ethical outcomes.
Applications and Use Cases
- Credit Card Fraud – Real-time flagging of unusual purchase behaviors.
- Insurance – Detecting staged accidents or fraudulent medical claims.
- Cybersecurity – Identifying phishing emails and malicious login attempts.
- Retail and E-commerce – Detecting fake reviews or fraudulent loyalty program activity.
- Banking – Predictive risk scoring for loan approvals
Benefits
- Detects complex, evolving fraud patterns that humans and static rules miss.
- Adapts continuously with new data (self-learning).
- Scales easily across millions of transactions.
- Reduces false negatives by identifying previously unseen fraud schemes.
Challenges and Limitations
- High-quality, labeled data is needed for accuracy.
- Models can be “black boxes,” making explainability difficult.
- Fraudsters may attempt to game or poison AI models.
- Implementation requires significant resources and expertise.
- Human investigators provide critical context, ethical judgment, and adaptive problem-solving that AI cannot replicate.
Network and Link Analysis
Organized fraud groups use multiple accounts, devices, and identities to obfuscate their actions, making it difficult to detect patterns when looking at transactions or behavior in isolation. Network and link analysis helps fill this gap by mapping the connections between individuals, accounts, and activity, revealing associations and relationships between seemingly disparate data points that would otherwise remain undetected.
Investigators use graph analysis to visualize transactions and connections, mapping how money, data, or logins flow through a network. Social network analysis allows investigators to visualize clusters of activity that may point to collusion. Link analysis also connects individual identifiers, such as email addresses, phone numbers, IP addresses, or social media profiles to form a larger network.
ShadowDragon’s suite of open-source intelligence (OSINT) tools, including Horizon™ Identity, SocialNet, and Horizon™ Monitor, can help investigators automate and accelerate identity resolution and network and link analysis. Horizon™ Identity resolves disparate identifiers to surface real-world identity profiles, SocialNet extracts connections from hundreds of online sources to reveal social and digital associations, and Horizon™ Monitor continuously monitors those networks to alert when new activity is detected.
Together, these tools give investigators the ability to build a complete intelligence picture starting from a single data point, uncovering hidden relationships, tracking evolving threat actor activity, and connecting online behaviors to real-world entities. This end-to-end visibility not only shortens investigation timelines but also strengthens attribution, enabling teams to make faster, more confident decisions in fraud detection, cybercrime investigations, and threat intelligence operations all within one platform.
Applications and Use Cases
- Financial Services – AML (anti-money laundering) investigations uncovering layered transactions.
- Insurance – Detecting fraud rings staging multiple fake accidents.
- E-commerce – Linking synthetic identities across multiple fake accounts.
- Telecom – Tracing collusion between insiders and external fraudsters.
- Law Enforcement – Mapping criminal organizations using digital footprints.
Benefits
- Reveals large-scale, organized fraud schemes.
- Provides visual insights for investigators and compliance teams.
- Helps connect seemingly unrelated data points.
- Strengthens AML and regulatory compliance.
Challenges and Limitations
- Requires advanced computing power for large datasets.
- Visualization can be complex with massive networks.
- Risk of false associations if data quality is poor.
The Case for a Layered Approach to Fraud Detection
There’s no silver bullet when it comes to fraud prevention. Rules-based algorithms are relatively quick and easy to set up but fail to evolve with criminals. Machine learning is good at detecting complex patterns but requires clean data and some human oversight.
Biometrics provide additional assurance, but they carry privacy concerns. Transaction monitoring works in real time but can generate noise if thresholds aren’t carefully fine-tuned. Network and link analysis tools, but visualization can be complex when mapping extensive fraud networks.
A layered approach brings together the strengths of these different methods. Identity verification keeps fraudsters from accessing your ecosystem in the first place. Rules-based models and transaction monitoring can be a simple first line of defense.
Machine learning and anomaly detection identify what gets through those initial filters. Network and link analysis can tie together disparate alerts and uncover the bigger schemes behind them.
No one method is foolproof against account takeovers, synthetic identities, or organized fraud rings on their own, but each can form part of a more holistic defense. With Horizon™ Identity, SocialNet, and Horizon™ Monitor, investigators can move quickly from a single suspicious data point to a mapped-out fraud network.
These tools shorten investigation timelines, strengthen attribution, and provide the clarity needed to act with confidence. Contact us for a demo today to learn how ShadowDragon can strengthen your fraud detection capabilities.
