Building a Device Fraud Detection System Using Unsupervised Learning

Image Source: Google

Author: Shivam Pal

Welcome to the Data-Driven Growth blog! Today, we’re diving into an exciting topic: Building a device fraud detection system using an unsupervised approach. Fraud detection is crucial for protecting businesses from the relentless tactics of fraudsters. One effective method is Device Fingerprinting, where we trace device footprints to classify users as either fraudsters or legitimate. In future posts, we’ll explore other methods of fraud detection.

Introduction to the Problem

In our digital era, fraudulent activities pose a significant threat to businesses, particularly those with consumer-facing applications. Fraudsters exploit system vulnerabilities, leading to substantial financial losses and damaging user trust. Device fraud is a major concern where malicious actors use multiple devices or fake identities to commit fraud. Detecting and mitigating such fraud is essential to maintaining the integrity and security of consumer applications.

Impact and Benefits of the Solution

Implementing a robust device fraud detection system can profoundly benefit businesses by

• Financial Savings: Proactively detecting and preventing fraud can save businesses millions annually.
• Enhanced Security: Safeguarding user data and transactions creates a secure environment.
• Improved User Trust: Ensuring legitimate use of the application builds user confidence and loyalty.
• Operational Efficiency: Automated detection systems reduce the need for manual reviews, saving time and resources.

Insights from the Problem

Our analysis reveals various fraudulent behaviors, including:

• Multiple Accounts on a Single Device: Some users log into as many as 30 different accounts from a single device.
• Single Account on Multiple Devices: Certain users log into a single account from over 300 devices, indicating potential fraud networks.
• High Login Rates: Some users log in as frequently as six times a day, which is highly unusual.

These patterns highlight the need for a sophisticated approach to detect and mitigate device fraud.

Approach to Solving the Problem

Our approach involves several key steps:

Data Collection

Gather relevant data such as user IDs, device IDs, IP addresses, and login timestamps. We have analyzed the data of over 1.1 Mn users.

Feature Engineering

Create features that capture device usage behaviors:

• Unique Device Count: The number of devices a single user ID logs into.
• Device Max Unique Count: The maximum number of unique logins on a device used by a user.
• Login Rate: Total logins divided by the number of days between account creation and the last login.

Modeling

We implemented the Isolation Forest algorithm to identify anomalies using a contamination rate of 2%.

def device_frauds(df, contamination=0.02):
  features = ['unique_device_count', 'device_max_unique_count', 'login_rate']
  clf = IsolationForest(contamination=contamination, random_state=42)
  clf.fit(df[features])
  df['device_anomaly_score'] = clf.decision_function(df[features])
  df['device_fraud'] = clf.predict(df[features])
  df['device_fraud'] = df['device_fraud'].map({1: 0, -1: 1})
  return df

Image Source: Google

Results and Impact

Implementing this solution led to significant improvements in fraud detection accuracy. The Isolation Forest model effectively identified outliers, which were potential fraudulent activities. Continuous monitoring and updating of the model maintained a high detection rate, reducing false positives and ensuring legitimate users were not adversely affected.

The solution’s impact included:

• Reduction in Fraudulent Activities: A notable decrease in detected fraudulent activities.
• Cost Savings: Lower operational costs due to reduced manual fraud reviews.
• Acquisition Cost Reduction: With better identification, we can spend money on more potential loyal customers

Potential Applications

The device fraud detection system can be adapted to various domains, including:

• E-commerce: Detecting fake accounts and fraudulent transactions.
• Finance: Identifying suspicious activities in banking and financial services.
• Healthcare: Ensuring the integrity of patient records and medical devices.
• Gaming: Preventing cheating and account fraud in online gaming platforms.

Leveraging this unsupervised learning approach allows businesses to enhance their security measures and protect their operations against fraudulent activities.

Thank you for reading! Stay tuned for the next article, where we’ll go deeper into the applications of machine learning for business growth.