Using Machine Learning for Fraud Detection

Fraud is a major issue that costs companies billions of dollars each year. Detecting fraud accurately and in real-time is critical for reducing losses from fraudulent activities. Machine learning provides a powerful set of techniques that can be used to build highly accurate fraud detection systems. In this technical writeup, we will explore how machine learning can be applied for fraud detection, with a focus on classification models, reducing chargebacks, and enabling real-time fraud prevention.

Classification Models for Fraud Detection

A common approach for fraud detection is to frame it as a binary classification problem - given an order or transaction, classify it as either fraudulent or legitimate. Machine learning classification algorithms like logistic regression, random forests, and neural networks can be trained on historical fraud data to build models that can accurately flag orders likely to be fraudulent.

The key requirement for building an effective classifier is to identify discriminative features that can help separate fraudulent and legitimate orders. Some examples of features that can be extracted from order/transaction data:

Customer attributes: information like email, phone, address, customer lifetime value, etc. Fraudsters may use disposable emails or stolen customer credentials.
Order details: billing/shipping address match, number of items, order value, time between orders, etc. Unusual patterns may indicate fraud.
Product details: electronics and luxury items have higher fraud rates. New or frequently refunded items also see more fraud.
Payment details: payment method, billing/shipping address match, declined payments, etc. can indicate suspicious activity.
IP and device data: IPs associated with fraud in the past, suspicious geolocations, device fingerprints, etc.

In addition to these explicit features, deep learning techniques like neural networks can also automatically learn higher-level abstract features from raw transaction data that may be predictive of fraud.

The classification models can be trained by feeding them labeled historical data of past transactions that are known to be fraudulent or legitimate. Once trained, the models can generate fraud probability scores for new unseen orders. A threshold can be set to determine when an order is likely enough to be fraudulent and should be flagged for further review.

Deep learning models that can be effective for fraud detection:

Convolutional Neural Networks (CNNs): CNNs are traditionally used for image classification, but their ability to automatically extract useful features through convolutional layers makes them useful for fraud detection as well. The convolutional layers can learn complex patterns from raw transaction data.
Recurrent Neural Networks (RNNs): RNNs like LSTMs are useful for sequential data like time-series transactions. They can detect anomalies and changes in patterns over time. The temporal historical patterns can inform fraud probability.
Autoencoders: Autoencoders are trained to reconstruct their inputs. They learn compressed representations of transaction data. Fraudulent transactions will often get reconstructed poorly, allowing detection.
Deep Belief Networks (DBNs): DBNs are probabilistic models with multiple hidden layers. They can learn complex representations and model high-level abstractions in transaction data for fraud classification.
Hybrid models: Combining different architectures like CNNs, RNNs and autoencoders can capitalize on their complementary strengths. The embeddings from each model can be combined.
Generative models: Generative adversarial networks and variational autoencoders can learn the distribution of normal transactions. Significant deviations can be flagged as anomalies and potential frauds.

While deep networks have strong representation power, their complexity can make real-time deployment challenging. Simpler linear models like logistic regression may suffice and are faster for production fraud screening. But deep learning provides more flexibility to handle complex fraud patterns.

Reducing Chargebacks with Fraud Detection

Chargebacks occur when customers dispute a charge and request their money back from the bank. High chargeback rates incur costs for companies and indicate lack of trust. An effective fraud detection system can help reduce chargebacks in two ways:

Block clearly fraudulent orders: Orders from known fraudsters or with extremely high fraud scores can simply be rejected at order placement time. This instantly eliminates the possibility of a chargeback for these confirmed frauds.
Early detection of likely fraud: For less clear cases, orders with a moderate-to-high fraud probability can be flagged for review. If fraudulent, these can be canceled proactively before shipment. This prevents goods exchange and significantly reduces chargeback likelihood compared to canceling after shipment.

The key indicators is the percentage of flagged orders that turn out to be actual frauds i.e. the precision of the fraud classifier. The higher this is, the more frauds are accurately caught, resulting in greater chargeback prevention.

Continuously retraining the model with new data also helps adapt to evolving fraud tactics and maintaining high precision over time.

Real-time Fraud Detection

To maximize fraud prevention, the prediction and scoring need to happen in real-time at order placement. Batch processing historical data to detect past fraud is less useful.

Some ways to enable real-time fraud screening:

Low-latency ML models: Models like logistic regression and random forests can be deployed for fast low-latency predictions. Simpler models may work better than heavy deep networks.
Feature engineering pipelines: The feature extraction process from transaction data needs to happen in real-time. This requires efficient data piping.
Scalable inference: Fraud models need to scale to large order volumes without delays. Cloud-based model serving solutions like AWS SageMaker can be used.
Incremental learning: The models can be continuously updated with new data in near real-time to adapt quicker to new fraud patterns.
Model ensembles: Combining predictions from multiple models can improve accuracy and robustness. The ensemble can be tuned to optimize fraud capture rate.
External analytics: Additional signals like blacklists, location data, device fingerprints etc. can complement the model predictions.
Manual review workflow: Human analysts can review and verify high risk orders flagged by the system. Their feedback further improves the model.

Together, these capabilities allow building an accurate, low-latency fraud screening system. This enables stopping fraudulent orders before fulfillment, drastically reducing chargeback costs.

Fraud detection is a crucial application area for machine learning in ecommerce. Classification algorithms trained on transaction data can identify high risk orders at order placement time. This allows blocking fraudulent orders, saving costs and improving customer trust. With the right data, models and infrastructure, machine learning can enable automated, real-time fraud prevention at scale.