In today’s digital age, online fraud is a significant threat to businesses, especially those in the fast-paced world of e-commerce and online services. Imagine a food delivery business, DeliverDinner, striving to provide seamless service while protecting itself from fraudulent activities. This is where Fraud Detection Machine Learning steps in as a powerful tool.
When DeliverDinner partners with a fraud prevention service like Ravelin, the integration begins with real-time transaction data flowing into an API.
Every action a customer takes on the DeliverDinner platform, from registration to adding items to their cart, generates data sent as JSON requests to the API. This rich dataset encompasses customer profiles and their entire interaction history with DeliverDinner. To effectively leverage this data for fraud detection using machine learning, we need to follow a structured approach, typically involving three key steps: data labeling, feature engineering, and model training.
Step 1: Data Labeling – Identifying Fraudulent Activities
The first crucial step in building a fraud detection machine learning model is to teach the system what constitutes fraud. This is achieved through data labeling. We examine historical customer data and identify instances of confirmed fraud.
Customers associated with chargebacks or those manually flagged as fraudulent by DeliverDinner’s fraud analysts are labeled as “fraudulent.” Conversely, the vast majority of customers who engage in legitimate transactions are labeled as “not fraudulent” or “genuine.” This labeled dataset becomes the foundation for training our machine learning model to distinguish between legitimate and fraudulent behaviors. Accurate and comprehensive labeling is paramount as the model’s learning and subsequent fraud detection capabilities are directly dependent on the quality of this initial step.
Step 2: Feature Engineering – Describing Customer Behavior in Computer Language
Once we have labeled our data, the next step is feature engineering. This involves translating complex customer behaviors and characteristics into a format that a machine learning model can understand and process – numerical features. Essentially, we are describing each customer in “computer language” by extracting and quantifying relevant information that can indicate fraudulent or genuine intent.
Feature engineering is inspired by the intuition of human fraud analysts. We consider the aspects they would examine to make a fraud judgment and then translate these into quantifiable features.
Examples of effective fraud detection features:
- Order Rate: Fraudsters often operate at a high velocity, attempting numerous fraudulent transactions quickly. We can quantify this by calculating the number of orders placed per week. A significantly higher order rate than typical customers can be a strong indicator of fraud.
- Email Address Analysis: Suspicious email addresses can be a red flag. Features can include the percentage of digits in the email address (excessive digits might suggest a randomly generated email) or analysis of email domain reputation.
- Delivery Location Intelligence: Delivery locations can provide valuable context. A delivery to a high-value residential address (e.g., penthouse apartment) might be less suspicious than delivery to a public space like a park. We can quantify this by calculating the historical fraud rate associated with specific delivery locations or types of locations.
- Payment Method Diversity: Genuine customers often use a consistent payment method. Fraudsters, on the other hand, might use multiple credit cards, especially stolen ones. The number of unique payment methods used within a specific timeframe can be a useful feature.
- Device Consistency: Legitimate users typically access services from a limited number of devices. A sudden shift to numerous new devices could be suspicious. We can track the number of unique devices used per account.
All these features, and many more, are transformed into numerical representations because machine learning models operate on numerical data. We organize these features into logical groups, creating a comprehensive profile for each customer, ready for the model to learn from.
Step 3: Model Training – Learning to Identify Fraud Patterns
With labeled data and engineered features, we are ready to train our fraud detection machine learning model. This is where the algorithm learns to differentiate between fraudulent and genuine customer behavior patterns.
We feed the model the “training data,” which consists of DeliverDinner customer data represented by our engineered features and their corresponding fraud labels (fraudulent or genuine). The algorithm analyzes this data to identify correlations and patterns that distinguish fraudsters from legitimate customers.
For instance, the model might learn that genuine DeliverDinner customers typically order around once a week, use the same payment card consistently, and have matching billing and delivery addresses. Conversely, it might identify patterns in fraudulent behavior such as multiple orders per week, frequent use of different cards, failed card registrations, and discrepancies between billing and delivery addresses.
The algorithm’s goal is to learn the optimal way to classify new, unseen customers based on whether their features resemble those of genuine or fraudulent customers from the training data. After training, the model can assess new customer transactions and generate a “fraud score.” This score represents the model’s confidence level that a given customer is fraudulent. Higher scores indicate a higher likelihood of fraud.
For most customers, the fraud score will be relatively low, reflecting the fact that genuine transactions far outnumber fraudulent ones. Based on the fraud score, businesses can implement different actions:
- Low Score: Allow the transaction to proceed automatically.
- Medium Score: Trigger a manual review or step-up authentication, such as a 3D Secure challenge, to verify the customer’s identity.
- High Score: Block the transaction and potentially suspend the account to prevent fraud.
Setting Risk Thresholds: Balancing Precision and Recall
Determining the appropriate fraud score thresholds for “allow,” “review,” and “prevent” actions is crucial and depends on the business’s risk tolerance and priorities. This involves understanding the concepts of precision and recall.
Precision measures the accuracy of our fraud prevention efforts. It answers the question: “Of all the transactions we flagged as fraudulent and prevented, what proportion were actually fraudulent?” High precision means fewer false positives – instances where legitimate customers are incorrectly flagged as fraudsters.
Recall measures our ability to detect actual fraud. It answers: “Of all the genuinely fraudulent transactions that occurred, what proportion did our model successfully identify and prevent?” High recall means fewer false negatives – instances where fraudulent transactions slip through undetected.
Precision & Recall in Practice:
- High Prevention Threshold (e.g., score of 95): Blocking only transactions with very high fraud scores results in high precision. We are confident that most blocked transactions are indeed fraudulent. False positive rates are low, minimizing disruption to genuine customers. However, recall might be low, meaning some fraudulent transactions with scores below 95 are missed.
- Low Prevention Threshold (e.g., score of 5): Blocking a large percentage of transactions, even those with low fraud scores, leads to high recall. We are likely to catch almost all fraudulent transactions. However, precision will be low, resulting in many false positives – blocking legitimate customers and causing friction.
Setting the right risk thresholds is a balancing act between precision and recall. Businesses must weigh the costs of false positives (customer dissatisfaction, lost revenue) against the costs of false negatives (fraud losses, chargebacks). The optimal thresholds are unique to each business and can be adjusted based on their specific risk appetite and operational priorities.
Understanding precision, recall, and risk thresholds is essential for evaluating the effectiveness of a fraud detection machine learning model and continuously improving its accuracy and performance. By leveraging machine learning, businesses like DeliverDinner can proactively combat online fraud, protect their revenue, and ensure a safer and more trustworthy experience for their genuine customers.
