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Digital Fraud Wiki

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Anomaly Detection: Uncovering Unseen Fraud Patterns

What is anomaly detection?

Anomaly detection is a data analysis technique used to identify data points or patterns that significantly deviate from expected behavior. These deviations, or anomalies, signal potentially concerning trends for investigators to review.

Anomaly detection spots unusual patterns in a few ways:

  1. Statistically: Identifying a data point’s Z-score, or how many standard deviations it falls outside the mean.
  2. Machine Learning (ML) Algorithms: Supervised ML models train on a labeled dataset containing both normal and anomalous instances, allowing the model to learn patterns and make predictions on new data. Unsupervised ML models can discover patterns within the data without using labeled instances.
  3. Time Series Analysis: Detects anomalies in time series data by analyzing trends, seasonality, and unexpected changes over time.
  4. Density-Based Methods: Identify anomalies based on the density of data points. Outliers are considered instances with significantly lower density.
  5. Ensemble Methods: Combine multiple models or techniques to improve overall performance in detecting anomalies.

The choice of the method depends on the characteristics of the data and the specific requirements of the application.

What are anomalies?

Anomalies are observations or patterns in data that deviate significantly from expected or normal behavior. Anomalies can manifest in various ways. No matter how they appear, detecting them is crucial in making proper use of a data set.

Types of anomalies

Point Anomalies

Individual data points that significantly differ from the majority of the dataset. These are often the most straightforward anomalies to detect.

Contextual Anomalies

Anomalies that are context-specific and depend on the conditions or circumstances. What’s considered normal may vary based on different factors or situations.

Collective Anomalies

Anomalies that can only be identified by considering a group or collection of data points together. These data points may not appear anomalous on their own, but their collective behavior is unusual. Network analysis helps more easily reveal collective anomalies.

Spatial Anomalies

Anomalies that occur in spatial data, such as geographic locations. Spatial anomalies can be identified by analyzing the spatial relationships between data points.

Time-series data anomalies

Anomalies that occur over time and disobey expected patterns in time-series data. Sudden spikes or drops in values can indicate temporal anomalies.

Anomaly detection and machine learning

Machine learning-powered anomaly detection enables systems to learn from historical data and generalize patterns to detect anomalies in new, unseen data.

In supervised anomaly detection, the model trains on a labeled dataset containing both normal and anomalous instances. Once it learns to differentiate between the two, it can identify anomalies in new, unlabeled data.

Unsupervised anomaly detection doesn’t rely on labeled data. Instead, the algorithm learns the inherent structure of the data and identifies instances that deviate from this learned structure. Clustering algorithms (e.g., k-means), density-based methods (e.g., DBSCAN), and other unsupervised techniques are commonly used.

Semi-supervised methods combine aspects of both supervised and unsupervised learning. These models are trained on a dataset with mostly normal instances but may have a small portion of labeled anomalies. The model generalizes from the normal instances and can then identify anomalies in new data.

Feature engineering, or selecting and engineering relevant features, is crucial for effective anomaly detection. Features should capture the distinguishing characteristics of normal and anomalous instances. Feature scaling, transformation, and extraction techniques contribute to improving model performance.

Ensemble methods involve combining multiple models to enhance the overall performance of anomaly detection. Combining diverse models, such as combining an isolation forest with a one-class SVM, can lead to better generalization and robustness.

It’s important to note that the choice of a particular machine learning approach depends on factors such as the nature of the data, the type of anomalies present, and the desired trade-off between false positives and false negatives. Anomaly detection using machine learning continues to be a dynamic field, with ongoing research and development to enhance the accuracy and efficiency of detection methods.

Anomaly detection use cases

  • Cybersecurity – Identifying unusual network traffic patterns, unauthorized access, or anomalous user behavior that may indicate a security breach or cyber attack.
  • Fraud detection – Detecting fraudulent activities in financial transactions, credit card usage, insurance claims, or any other financial processes by identifying anomalous patterns.
  • Network monitoring – Monitoring network infrastructure to identify anomalies in traffic, latency, or device behavior that may indicate network issues or security threats.
  • Credit scoring – Detecting anomalous patterns in credit history, loan applications, or financial behavior to prevent identity theft and improve credit scoring accuracy.
  • Predictive maintenance – Monitoring equipment, machinery, or vehicles to detect anomalies in performance or behavior, allowing for proactive maintenance and minimizing downtime.
  • Log and event analysis – Analyzing logs and event data to detect anomalies in system behavior, user activities, or application performance, which may indicate security breaches or system failures.
  • Network intrusion detection – Identifying abnormal network traffic patterns that may indicate malicious activities, such as denial-of-service attacks or attempts to exploit vulnerabilities.
  • Retail – Monitoring sales transactions, customer behavior, or inventory levels to detect anomalies, prevent fraud, and optimize retail operations.

Why anomaly detection is important in fraud prevention

Fraud prevention systems need to identify anomalies to unveil fraudulent activity and prevent it. In the modern payment landscape where faster and real-time payments are standard, anomaly detection helps quickly spot and mitigate fraud patterns as they happen.

Anomaly detection helps power a handful of key fraud prevention methods, including:

  • Early detection of potential fraudulent activities before they can cause significant harm.
  • Adaptability to evolving patterns of fraud without the need for constant manual intervention or rule updates.
  • Identifying unknown or novel fraud patterns traditional rule-based systems might miss.
  • Reducing false positives by learning from normal user behavior.
    Detecting insider threats.
  • Real-time monitoring and response when suspicious activities are detected.
  • Account takeover prevention.
  • Minimizing fraud losses by preventing fraudulent activities before they escalate.
  • Compliance with regulations around preventing fraud and protecting customer data.
    Upholding customer trust and organizational reputation.

How DataVisor’s platform leverages anomaly detection

DataVisor’s platform employs both supervised and unsupervised machine learning to detect anomalies and suspicious patterns without relying on predefined rules. This allows the platform to identify emerging fraud tactics that often go unnoticed by traditional systems.

Through real-time detection methods, the DataVisor platform adapts to new fraud scenarios leveraging data signal orchestration and end-to-end workflow automation. The holistic nature of this platform increases both detection accuracy and efficiency while lowering false positive rates and providing industry-leading scalability.

Learn how anomaly detection helped NASA FCU reduce fraud losses, enhance member trust, and streamline their operations by reading our full case study. To see DataVisor’s anomaly detection capabilities for yourself, book a customized demo with our team.