Machine Learning for Fraud Detection in Payments

05.09.2024

Traditional rule-based fraud detection struggles against sophisticated attack patterns. Machine learning offers powerful alternatives, but implementation in regulated environments requires careful consideration.

The Fraud Detection Challenge

Payment fraud is an arms race. Fraudsters constantly evolve their techniques, and static rules quickly become obsolete. A fraud ring might probe your defences, identify patterns that trigger rules, and then craft attacks that slip through.

Machine learning offers dynamic defence—models that learn from data and adapt to new patterns. But implementing ML in production fraud detection brings its own challenges.

Rule-Based vs. ML-Based Detection

Traditional Rules

Advantages: Transparent, auditable, easy to adjust
Disadvantages: Brittle, high false positive rates, reactive

Example rule: "Block transactions over £1,000 to new payees initiated outside business hours"

This catches some fraud but blocks legitimate transactions and is easily evaded.

ML-Based Detection

Advantages: Adapts to new patterns, better precision, considers complex interactions
Disadvantages: Less transparent, requires significant data, ongoing maintenance

ML models consider hundreds of features simultaneously, detecting subtle patterns humans would miss.

Choosing Your Approach

Supervised Learning

Train models on labelled data (known fraud vs. legitimate transactions):

Requires historical fraud labels
Good at detecting fraud similar to past incidents
Struggles with novel attack patterns
Common algorithms: XGBoost, Random Forest, Neural Networks

Unsupervised Learning

Detect anomalies without labelled data:

Doesn't require fraud labels
Can detect novel patterns
Higher false positive rates
Common approaches: Isolation Forest, Autoencoders, Clustering

Hybrid Approach

In practice, combining both works best:

Supervised models for known fraud patterns
Anomaly detection for unusual behaviour
Rules for hard constraints (sanctions, velocity limits)

Feature Engineering

The quality of fraud models depends heavily on features. Key categories:

// Example feature categories
$features = [
    // Transaction features
    'amount' => $transaction->amount,
    'currency' => $transaction->currency,
    'payment_method' => $transaction->method,

    // Temporal features
    'hour_of_day' => $transaction->created_at->hour,
    'day_of_week' => $transaction->created_at->dayOfWeek,
    'days_since_last_txn' => $this->daysSinceLastTransaction($user),

    // Velocity features
    'txn_count_1h' => $this->transactionCount($user, '1 hour'),
    'txn_count_24h' => $this->transactionCount($user, '24 hours'),
    'unique_payees_7d' => $this->uniquePayees($user, '7 days'),

    // Behavioural features
    'typical_amount_ratio' => $amount / $user->average_amount,
    'new_payee' => $this->isNewPayee($user, $payee),
    'new_device' => $this->isNewDevice($user, $device),

    // Network features
    'device_fraud_rate' => $this->deviceFraudRate($device),
    'payee_fraud_rate' => $this->payeeFraudRate($payee),
];

Handling Class Imbalance

Fraud is rare (typically 0.1-0.5% of transactions). This creates training challenges:

Oversampling: Duplicate fraud cases (SMOTE, ADASYN)
Undersampling: Reduce legitimate cases
Cost-sensitive learning: Weight fraud cases higher
Anomaly detection: Train only on legitimate transactions

Model Explainability

Regulators and operations teams need to understand why transactions are blocked:

SHAP values: Show feature contributions to predictions
LIME: Local interpretable model-agnostic explanations
Feature importance: Global ranking of feature influence

For each declined transaction, generate human-readable reasons: "Transaction blocked: unusually large amount (5x typical), new device, rapid succession of attempts."

Operational Considerations

Latency Requirements

Fraud scoring must happen in real-time (typically <100ms). This constrains:

Model complexity
Feature computation
Infrastructure design

Model Monitoring

Production models degrade over time as fraud patterns change:

Track model performance metrics continuously
Monitor feature distributions for drift
Alert on performance degradation
Retrain on recent data regularly

Feedback Loops

Capture outcomes to improve models:

Confirmed fraud cases
False positive investigations
Customer disputes and chargebacks

Conclusion

Machine learning significantly improves fraud detection over static rules, but success requires attention to feature engineering, model explainability, and operational monitoring. The best fraud systems combine ML models with domain expertise and continuous improvement cycles. In regulated environments, maintaining audit trails and explainable decisions is as important as catching fraud.

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