Introduction to Anti-Fraud Systems and the Role of Machine Learning

[Student]

Anti-fraud systems are comprehensive platforms used in the financial industry (banks, payment services like Visa, Mastercard, PayPal, or Tinkoff) to prevent fraud. They analyze transactions in real time to distinguish legitimate from suspicious ones. Carders are cybercriminals who specialize in stealing bank card data (numbers, CVV, expiration dates) through phishing, skimming, or database leaks. They use this data for unauthorized purchases, withdrawals, or sales on the dark web.

Predictive detection involves not simply reacting to known signs of fraud (as in rule-based systems), but forecasting risk based on historical and current data. Machine learning (ML) algorithms, trained on billions of transactions, play a key role here, identifying hidden patterns. According to the Nilson Report (2023), global fraud losses have exceeded $5 trillion, and machine learning helps reduce them by 20–50% (FICO, 2024). This is an educational overview: we'll walk you through the process step by step, with algorithm examples, math, and practical case studies, so you can understand how it works (and even implement a basic model in Python).

Step 1: Data Collection and Preparation​

Data is the foundation of any machine learning model. Anti-fraud systems collect multimodal transaction data to create a complete user profile and operations.

Key data types​

• Transactional: Amount, currency, type (online/offline), merchant.
• User: Account ID, transaction history, demographics (age, gender - anonymized).
• Contextual: Time (hour, day of the week), geolocation (billing/shipping address, IP), device (OS, browser, fingerprint - a unique device fingerprint based on canvas, fonts).
• Behavioral: Data entry speed, click patterns, login frequency.
• External: Integration with blacklists (e.g. from Visa's Shared Service Provider) or breach data (Have I Been Pwned).

Typical "red flags" for carders include: a transaction from a new country after logging in with a VPN, a series of small test payments ($1–5) before a large withdrawal, or the use of a proxy to mask the IP.

Data preparation​

1. Collection: Stream processing with Apache Kafka or AWS Kinesis - data arrives in real time (latency < 100 ms).
2. Cleaning and augmentation: Removing duplicates, handling gaps (imputation, for example, using the average value). Class balancing: fraudulent transactions account for ~0.1–1% of the total volume, so techniques like SMOTE (Synthetic Minority Oversampling Technique) are used to generate synthetic fraud samples.
3. Feature engineering: Creating new features, such as:
   • Velocity score: Number of transactions per hour.
   • Distance score: Distance between billing and IP addresses (according to Haversine formula: d=2Rarcsin⁡(sin⁡2(Δϕ2)+cos⁡ϕ1cos⁡ϕ2sin⁡2(Δλ2)) d = 2R \arcsin(\sqrt{\sin^2(\frac{\Delta\phi}{2}) + \cos\phi_1 \cos\phi_2 \sin^2(\frac{\Delta\lambda}{2})}) d=2Rarcsin(sin2(2Δϕ)+cosϕ1cosϕ2sin2(2Δλ)), where R is the radius of the Earth).
   • Entropy of device: User-agent entropy for bot detection.

Educational example: Imagine a dataset of 1 million transactions (like on Kaggle: "Credit Card Fraud Detection"). The target variable is a binary label (fraud=1, legit=0). After training: 80% train, 20% test split.

Step 2: Selecting and training models​

Machine learning models are trained on labeled data (supervised learning), but unsupervised methods are used for unknown attacks. Training is performed on GPU clusters (TensorFlow/PyTorch), with cross-validation (k-fold=5).

Basic Algorithms and Their Mathematics​

Algorithm	Type	How it works	Application to carders	Mathematics (simplified)	Evaluation metrics
Random Forest	Supervised (classification)	An ensemble of decision trees; each tree votes for a class. Robust against overfitting.	Classifies by combinations of features (eg, high amount + new location = fraud).	Gini impurity: Gini=1−∑pi2 Gini = 1 - \sum p_i^2 Gini=1−∑pi2, where pi p_i pi is the class probability. Bootstrap sampling for variety.	Precision=0.95, Recall=0.90 (F1=0.92).
XGBoost (Gradient Boosting)	Supervised	Progressive improvement of weak models; gradient descent by error.	Predicts risk based on gradients (eg, chain of test transactions).	Обновление: Fm(x)=Fm−1(x)+νhm(x) F_m(x) = F_{m-1}(x) + \nu h_m(x) Fm(x)=Fm−1(x)+νhm(x), где ν \nu ν — learning rate, hm h_m hm — дерево.	AUC-ROC=0.98 (better for unbalanced data).
Isolation Forest	Unsupervised (anomaly detection)	"Isolates" anomalies with short paths in random trees.	Identifies rare patterns, like a single transaction at 3am from Africa.	Path length: Anomalies are isolated faster; score = 2−E(h(x))/c 2^{-E(h(x))/c } 2−E(h(x))/c , where E(h) is the average path length.	Silhouette score=0.7 for clustering.
Autoencoder (neural network)	Unsupervised/Deep Learning	Compresses data into latent space, reconstructs; reconstruction errors are anomalies.	Detects fake behavior (eg, unnatural clicks).	Loss: MSE = 1n∑(x−x^)2 \frac{1}{n} \sum (x - \hat{x})^2 n1∑(x−x^)2. Encoder: z=σ(Wx+b) z = \sigma(Wx + b) z=σ(Wx+b).	Reconstruction error > threshold=0.1.
LSTM (Recurrent Neural Network)	Supervised (sequences)	Processes time series; remembers long-term dependencies.	Predicts attack chains (eg, login → test → theft).	Gate equations: Forget ft=σ(Wf[ht−1,xt]) f_t = \sigma(W_f [h_{t-1}, x_t]) ft=σ(Wf[ht−1,xt]); Output ot=σ(Wo[ht−1,xt]) o_t = \sigma(W_o [h_{t-1}, x_t]) ot=σ(Wo[ht−1,xt]).	Accuracy=96% on sequences.

Training process:

• Initialization: Random weights.
• Forward pass: Prediction.
• Backward pass: Gradient descent (Adam optimizer, lr=0.001).
• Epochs: 50–100, early stopping by validation loss.
• For carders: Models focus on "card-not-present" fraud (CNP), where there is no physical card.

Educational code example (Python with scikit-learn):

from sklearn.ensemble import IsolationForest
from sklearn.model_selection import train_test_split
# Предполагаем X — фичи, y — labels
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
model = IsolationForest(contamination=0.01, random_state=42)  # 1% аномалий
model.fit(X_train)
predictions = model.predict(X_test)  # -1 = anomaly (fraud)

Stage 3: Predictive Analysis and Scoring​

In production, the model works in real time: transaction → features → inference (<50 ms).

• Scoring: The model produces a probability P(fraud|features) using the logistic function (for binary classification): P=11+e−z P = \frac{1}{1 + e^{-z}} P=1+e−z1, where z = a linear combination of features.
• Threshold: If P > 0.7, the "high risk" flag is activated. For carders:
  • Temporal: ARIMA-like models for forecasting spikes (eg, +200% transactions at night).
  • Geo: Graph Neural Networks (ML) for carder networks (eg, IP clusters from a single bot).
  • Behavior: Biometrics (keystroke dynamics) - MO compares with baseline profile.

Update: Online learning (e.g., with Vowpal Wabbit) – the model is retrained on new fraudulent examples daily, without complete retraining.

Case: At PayPal, XGBoost + LSTM reduces fraud by 25% (2023 report), blocking 90% of attacks before they are charged.

Stage 4: Integration, Action, and Monitoring​

• Integration: API with payment gateways (e.g., Stripe). Triggering: 3D Secure (SMS/biometrics), hold (freeze), or decline.
• Actions: Automatic vs. manual review (human-in-the-loop for edge cases).
• Monitoring: A/B testing of models; drift detection (if data distribution changes, eg, new VPNs).

Benefits, Challenges, and Ethical Considerations​

Aspect	Advantages	Challenges	Solutions
Efficiency	Accuracy 95%+ vs. 70% in rule-based; scalability (1M TPS).	Adversarial attacks: Carders "poison" data (e.g., add noise).	Robust training (adversarial examples in the data).
Adaptability	Reacts to evolution (eg, AI-generated fake profiles).	Data imbalance and cold start (new users).	Transfer learning from pre-trained models.
Ethics/Privacy	Reduces losses for everyone.	Bias (e.g., geo-flags discriminate against certain regions); GDPR compliance.	FairML: Audit на bias (e.g., AIF360 toolkit).
Price	ROI: $7 saved per $1 invested (McKinsey, 2024).	High computation costs.	Edge computing on devices.

Conclusion and recommendations for training​

Machine learning in antifraud is a dynamic field where predictive models transform data chaos into actionable insights, saving billions from carders. For practice:

• Datasets: Kaggle "IEEE-CIS Fraud Detection".
• Courses: Coursera "Machine Learning for Fraud Detection" (or Andrew Ng's ML Specialization).
• Книги: "Hands-On Machine Learning with Scikit-Learn" by Aurélien Géron.

If you want to dive deeper into a specific algorithm or code, please let me know!