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Detecting Financial Fraud using Anomaly Detection Techniques

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Updated Feb 6, 2026

Introduction

Financial fraud detection is one of the most critical applications of machine learning in the banking and fintech industries. According to recent estimates, fraudulent transactions cost the global economy billions of dollars annually. In this fifth episode of our Mastering Financial Data Science with Kaggle series, we’ll dive deep into anomaly detection techniques specifically designed to identify fraudulent transactions in highly imbalanced datasets.

In previous episodes, we explored feature engineering for time-series data and building credit risk models. Now we’ll leverage those foundations to tackle one of the most challenging problems in financial ML: detecting rare fraudulent events among millions of legitimate transactions.

Understanding the Fraud Detection Challenge

The Imbalanced Data Problem

Credit card fraud detection presents a unique challenge: class imbalance. In real-world datasets, fraudulent transactions typically represent less than 0.1% to 2% of all transactions. This extreme imbalance creates several problems:

  • Standard classification algorithms optimize for overall accuracy, which can achieve 99%+ by simply predicting “legitimate” for every transaction
  • Rare fraud patterns are difficult for models to learn
  • Evaluation metrics like accuracy become misleading
  • The cost of false negatives (missing fraud) vastly outweighs false positives

Kaggle’s Credit Card Fraud Dataset

We’ll use the popular Credit Card Fraud Detection dataset from Kaggle, which contains 284,807 transactions with only 492 frauds (0.172% fraud rate). The features V1-V28 are PCA-transformed for confidentiality, along with Time, Amount, and the binary Class label.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import warnings
warnings.filterwarnings('ignore')

# Load the dataset
df = pd.read_csv('creditcard.csv')

print(f"Dataset shape: {df.shape}")
print(f"\nClass distribution:")
print(df['Class'].value_counts())
print(f"\nFraud percentage: {df['Class'].sum() / len(df) * 100:.3f}%")

# Visualize class imbalance
fig, ax = plt.subplots(1, 2, figsize=(12, 4))
df['Class'].value_counts().plot(kind='bar', ax=ax[0], color=['#2ecc71', '#e74c3c'])
ax[0].set_title('Transaction Class Distribution')
ax[0].set_xlabel('Class (0=Legitimate, 1=Fraud)')
ax[0].set_ylabel('Count')

# Amount distribution by class
df[df['Class'] == 0]['Amount'].apply(np.log1p).hist(bins=50, alpha=0.5, label='Legitimate', ax=ax[1])
df[df['Class'] == 1]['Amount'].apply(np.log1p).hist(bins=50, alpha=0.5, label='Fraud', ax=ax[1])
ax[1].set_title('Log-transformed Amount Distribution')
ax[1].set_xlabel('log(Amount + 1)')
ax[1].legend()
plt.tight_layout()
plt.show()

Handling Imbalanced Data: Resampling Techniques

1. Random Undersampling

The simplest approach is to randomly remove majority class samples to balance the dataset:

from sklearn.utils import resample

# Separate majority and minority classes
df_majority = df[df['Class'] == 0]
df_minority = df[df['Class'] == 1]

# Undersample majority class to match minority size
df_majority_downsampled = resample(df_majority,
                                    replace=False,
                                    n_samples=len(df_minority),
                                    random_state=42)

# Combine minority class with downsampled majority class
df_undersampled = pd.concat([df_majority_downsampled, df_minority])

print(f"Undersampled dataset shape: {df_undersampled.shape}")
print(df_undersampled['Class'].value_counts())

Pros: Simple, fast, reduces training time
Cons: Loses potentially valuable majority class information

2. Random Oversampling

Alternatively, we can duplicate minority class samples:

# Oversample minority class to match majority size
df_minority_upsampled = resample(df_minority,
                                  replace=True,
                                  n_samples=len(df_majority),
                                  random_state=42)

df_oversampled = pd.concat([df_majority, df_minority_upsampled])

print(f"Oversampled dataset shape: {df_oversampled.shape}")
print(df_oversampled['Class'].value_counts())

Pros: Retains all original information
Cons: Overfitting risk due to exact duplicates, increased training time

3. SMOTE (Synthetic Minority Over-sampling Technique)

SMOTE generates synthetic minority class samples by interpolating between existing minority samples:

from imblearn.over_sampling import SMOTE

# Prepare features and target
X = df.drop('Class', axis=1)
y = df['Class']

# Split data first (important: apply SMOTE only to training data)
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.3, random_state=42, stratify=y
)

# Apply SMOTE to training data
smote = SMOTE(random_state=42, k_neighbors=5)
X_train_smote, y_train_smote = smote.fit_resample(X_train, y_train)

print(f"Original training set: {X_train.shape}")
print(f"SMOTE training set: {X_train_smote.shape}")
print(f"\nClass distribution after SMOTE:")
print(pd.Series(y_train_smote).value_counts())

SMOTE works by selecting a minority sample xix_i and one of its kk nearest minority neighbors xnnx_{nn}, then creating a synthetic sample:

xnew=xi+λ(xnn−xi)x_{new} = x_i + \lambda (x_{nn} – x_i)

where:
– xnewx_{new} is the synthetic sample
– xix_i is the selected minority sample
– xnnx_{nn} is a randomly chosen neighbor from the kk nearest neighbors
– λ∈[0,1]\lambda \in [0,1] is a random number controlling interpolation

Pros: Creates realistic synthetic samples, reduces overfitting compared to simple oversampling
Cons: Can create noisy samples in overlapping regions, computationally expensive for large datasets

Comparing Resampling Strategies

from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, roc_auc_score

# Standardize features
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
X_train_smote_scaled = scaler.fit_transform(X_train_smote)

# Train models with different sampling strategies
results = {}

# 1. No resampling (baseline)
rf_baseline = RandomForestClassifier(n_estimators=100, random_state=42, n_jobs=-1)
rf_baseline.fit(X_train_scaled, y_train)
y_pred_baseline = rf_baseline.predict(X_test_scaled)
results['Baseline'] = roc_auc_score(y_test, rf_baseline.predict_proba(X_test_scaled)[:, 1])

# 2. SMOTE
rf_smote = RandomForestClassifier(n_estimators=100, random_state=42, n_jobs=-1)
rf_smote.fit(X_train_smote_scaled, y_train_smote)
y_pred_smote = rf_smote.predict(X_test_scaled)
results['SMOTE'] = roc_auc_score(y_test, rf_smote.predict_proba(X_test_scaled)[:, 1])

print("ROC-AUC Scores:")
for method, score in results.items():
    print(f"{method}: {score:.4f}")

Anomaly Detection Algorithms

Isolation Forest

Isolation Forest is particularly well-suited for fraud detection because it explicitly targets anomalies rather than trying to model normal behavior. The algorithm works on a simple principle: anomalies are easier to isolate than normal points.

How Isolation Forest Works

The algorithm builds an ensemble of isolation trees by:
1. Randomly selecting a feature
2. Randomly selecting a split value between the min and max of that feature
3. Recursively partitioning the data

Anomalies require fewer splits to isolate, resulting in shorter path lengths. The anomaly score is computed as:

s(x,n)=2−E(h(x))c(n)s(x, n) = 2^{-\frac{E(h(x))}{c(n)}}

where:
– E(h(x))E(h(x)) is the average path length of sample xx across all trees
– c(n)c(n) is the average path length of unsuccessful search in a Binary Search Tree, used for normalization:
c(n)=2H(n−1)−2(n−1)nc(n) = 2H(n-1) – \frac{2(n-1)}{n}
– H(n)H(n) is the harmonic number: ln⁡(n)+γ≈0.5772\ln(n) + \gamma \approx 0.5772 (Euler’s constant)
– nn is the number of samples

Scores close to 1 indicate anomalies, close to 0.5 indicate normal points.

from sklearn.ensemble import IsolationForest
from sklearn.metrics import classification_report, confusion_matrix, precision_recall_curve

# Train Isolation Forest on original (imbalanced) data
iforest = IsolationForest(
    n_estimators=100,
    max_samples='auto',
    contamination=0.002,  # Expected fraud rate
    random_state=42,
    n_jobs=-1
)

# Fit on training data
iforest.fit(X_train_scaled)

# Predict on test data (-1 for anomaly, 1 for normal)
y_pred_if = iforest.predict(X_test_scaled)

# Convert to binary labels (0=normal, 1=fraud)
y_pred_if_binary = np.where(y_pred_if == -1, 1, 0)

# Get anomaly scores (lower = more anomalous)
anomaly_scores = -iforest.score_samples(X_test_scaled)

print("Isolation Forest Results:")
print(classification_report(y_test, y_pred_if_binary))
print(f"\nROC-AUC: {roc_auc_score(y_test, anomaly_scores):.4f}")

# Confusion matrix
cm = confusion_matrix(y_test, y_pred_if_binary)
plt.figure(figsize=(8, 6))
sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', 
            xticklabels=['Legitimate', 'Fraud'],
            yticklabels=['Legitimate', 'Fraud'])
plt.title('Isolation Forest Confusion Matrix')
plt.ylabel('True Label')
plt.xlabel('Predicted Label')
plt.show()

Local Outlier Factor (LOF)

LOF measures the local deviation of density of a sample with respect to its neighbors:

from sklearn.neighbors import LocalOutlierFactor

# LOF for novelty detection (fit on normal transactions only)
X_train_normal = X_train_scaled[y_train == 0]

lof = LocalOutlierFactor(
    n_neighbors=20,
    contamination=0.002,
    novelty=True  # Enable prediction on new data
)

lof.fit(X_train_normal)

# Predict on test data
y_pred_lof = lof.predict(X_test_scaled)
y_pred_lof_binary = np.where(y_pred_lof == -1, 1, 0)

print("Local Outlier Factor Results:")
print(classification_report(y_test, y_pred_lof_binary))
print(f"\nROC-AUC: {roc_auc_score(y_test, -lof.score_samples(X_test_scaled)):.4f}")

One-Class SVM

One-Class SVM learns a decision boundary around normal data:

from sklearn.svm import OneClassSVM

# Train on normal transactions only
ocsvm = OneClassSVM(
    kernel='rbf',
    gamma='auto',
    nu=0.002  # Upper bound on fraction of outliers
)

ocsvm.fit(X_train_normal)

# Predict on test data
y_pred_ocsvm = ocsvm.predict(X_test_scaled)
y_pred_ocsvm_binary = np.where(y_pred_ocsvm == -1, 1, 0)

print("One-Class SVM Results:")
print(classification_report(y_test, y_pred_ocsvm_binary))

Evaluation Metrics for Imbalanced Classification

Why Accuracy is Misleading

With 0.172% fraud rate, a model that predicts “legitimate” for every transaction achieves 99.828% accuracy but catches zero fraud. We need better metrics.

Precision, Recall, and F1-Score

Precision (Positive Predictive Value):
Precision=TPTP+FP\text{Precision} = \frac{TP}{TP + FP}

What percentage of fraud predictions are correct?

Recall (Sensitivity, True Positive Rate):
Recall=TPTP+FN\text{Recall} = \frac{TP}{TP + FN}

What percentage of actual frauds are detected?

F1-Score (harmonic mean of precision and recall):
F1=2×Precision×RecallPrecision+RecallF1 = 2 \times \frac{\text{Precision} \times \text{Recall}}{\text{Precision} + \text{Recall}}

where:
– TPTP = True Positives (correctly identified frauds)
– FPFP = False Positives (legitimate flagged as fraud)
– FNFN = False Negatives (missed frauds)
– TNTN = True Negatives (correctly identified legitimate)

from sklearn.metrics import precision_score, recall_score, f1_score, average_precision_score

def evaluate_fraud_model(y_true, y_pred, y_scores=None, model_name="Model"):
    """
    Comprehensive evaluation for fraud detection models
    """
    print(f"\n{'='*50}")
    print(f"{model_name} Evaluation")
    print(f"{'='*50}")

    # Basic metrics
    print(f"Precision: {precision_score(y_true, y_pred):.4f}")
    print(f"Recall: {recall_score(y_true, y_pred):.4f}")
    print(f"F1-Score: {f1_score(y_true, y_pred):.4f}")

    if y_scores is not None:
        print(f"ROC-AUC: {roc_auc_score(y_true, y_scores):.4f}")
        print(f"PR-AUC: {average_precision_score(y_true, y_scores):.4f}")

    # Confusion matrix breakdown
    cm = confusion_matrix(y_true, y_pred)
    tn, fp, fn, tp = cm.ravel()

    print(f"\nConfusion Matrix:")
    print(f"  True Negatives: {tn}")
    print(f"  False Positives: {fp}")
    print(f"  False Negatives: {fn}")
    print(f"  True Positives: {tp}")

    # Cost analysis (assuming fraud costs $100, false alarm costs $1)
    fraud_cost = 100
    false_alarm_cost = 1
    total_cost = (fn * fraud_cost) + (fp * false_alarm_cost)
    print(f"\nEstimated Cost: ${total_cost:,.2f}")
    print(f"  Missed fraud cost: ${fn * fraud_cost:,.2f}")
    print(f"  False alarm cost: ${fp * false_alarm_cost:,.2f}")

# Evaluate all models
evaluate_fraud_model(y_test, y_pred_baseline, 
                     rf_baseline.predict_proba(X_test_scaled)[:, 1], 
                     "Random Forest (Baseline)")
evaluate_fraud_model(y_test, y_pred_smote, 
                     rf_smote.predict_proba(X_test_scaled)[:, 1], 
                     "Random Forest (SMOTE)")
evaluate_fraud_model(y_test, y_pred_if_binary, 
                     anomaly_scores, 
                     "Isolation Forest")

ROC Curve and AUC

The Receiver Operating Characteristic (ROC) curve plots True Positive Rate vs False Positive Rate at various threshold settings:

from sklearn.metrics import roc_curve, auc

# Compute ROC curves for different models
fpr_baseline, tpr_baseline, _ = roc_curve(y_test, rf_baseline.predict_proba(X_test_scaled)[:, 1])
fpr_smote, tpr_smote, _ = roc_curve(y_test, rf_smote.predict_proba(X_test_scaled)[:, 1])
fpr_if, tpr_if, _ = roc_curve(y_test, anomaly_scores)

# Plot ROC curves
plt.figure(figsize=(10, 8))
plt.plot(fpr_baseline, tpr_baseline, label=f'RF Baseline (AUC = {auc(fpr_baseline, tpr_baseline):.4f})', linewidth=2)
plt.plot(fpr_smote, tpr_smote, label=f'RF + SMOTE (AUC = {auc(fpr_smote, tpr_smote):.4f})', linewidth=2)
plt.plot(fpr_if, tpr_if, label=f'Isolation Forest (AUC = {auc(fpr_if, tpr_if):.4f})', linewidth=2)
plt.plot([0, 1], [0, 1], 'k--', label='Random Classifier', linewidth=1)

plt.xlabel('False Positive Rate', fontsize=12)
plt.ylabel('True Positive Rate', fontsize=12)
plt.title('ROC Curves: Fraud Detection Models', fontsize=14)
plt.legend(loc='lower right', fontsize=10)
plt.grid(alpha=0.3)
plt.tight_layout()
plt.show()

Precision-Recall Curve

For highly imbalanced datasets, the Precision-Recall (PR) curve is often more informative than ROC:

from sklearn.metrics import precision_recall_curve

# Compute PR curves
precision_baseline, recall_baseline, _ = precision_recall_curve(y_test, rf_baseline.predict_proba(X_test_scaled)[:, 1])
precision_smote, recall_smote, _ = precision_recall_curve(y_test, rf_smote.predict_proba(X_test_scaled)[:, 1])
precision_if, recall_if, _ = precision_recall_curve(y_test, anomaly_scores)

# Plot PR curves
plt.figure(figsize=(10, 8))
plt.plot(recall_baseline, precision_baseline, 
         label=f'RF Baseline (AP = {average_precision_score(y_test, rf_baseline.predict_proba(X_test_scaled)[:, 1]):.4f})', linewidth=2)
plt.plot(recall_smote, precision_smote, 
         label=f'RF + SMOTE (AP = {average_precision_score(y_test, rf_smote.predict_proba(X_test_scaled)[:, 1]):.4f})', linewidth=2)
plt.plot(recall_if, precision_if, 
         label=f'Isolation Forest (AP = {average_precision_score(y_test, anomaly_scores):.4f})', linewidth=2)

plt.xlabel('Recall', fontsize=12)
plt.ylabel('Precision', fontsize=12)
plt.title('Precision-Recall Curves: Fraud Detection Models', fontsize=14)
plt.legend(loc='best', fontsize=10)
plt.grid(alpha=0.3)
plt.tight_layout()
plt.show()

Average Precision (AP) summarizes the PR curve as the weighted mean of precisions at each threshold:

AP=∑n(Rn−Rn−1)PnAP = \sum_{n} (R_n – R_{n-1}) P_n

where PnP_n and RnR_n are precision and recall at the nn-th threshold.

Advanced Techniques: Ensemble and Threshold Optimization

Cost-Sensitive Learning

Assign different misclassification costs to balance business impact:

from sklearn.ensemble import RandomForestClassifier

# Define class weights (fraud is 100x more important)
class_weight = {0: 1, 1: 100}

rf_weighted = RandomForestClassifier(
    n_estimators=100,
    class_weight=class_weight,
    random_state=42,
    n_jobs=-1
)

rf_weighted.fit(X_train_scaled, y_train)
y_pred_weighted = rf_weighted.predict(X_test_scaled)

evaluate_fraud_model(y_test, y_pred_weighted, 
                     rf_weighted.predict_proba(X_test_scaled)[:, 1], 
                     "Random Forest (Cost-Sensitive)")

Threshold Optimization

Instead of using default 0.5 threshold, optimize based on business metrics:

def find_optimal_threshold(y_true, y_scores, fraud_cost=100, false_alarm_cost=1):
    """
    Find threshold that minimizes total cost
    """
    precision_vals, recall_vals, thresholds = precision_recall_curve(y_true, y_scores)

    costs = []
    for i, threshold in enumerate(thresholds):
        y_pred = (y_scores >= threshold).astype(int)
        cm = confusion_matrix(y_true, y_pred)
        tn, fp, fn, tp = cm.ravel()

        total_cost = (fn * fraud_cost) + (fp * false_alarm_cost)
        costs.append(total_cost)

    optimal_idx = np.argmin(costs)
    optimal_threshold = thresholds[optimal_idx]

    return optimal_threshold, costs[optimal_idx]

# Find optimal threshold for RF model
optimal_threshold, min_cost = find_optimal_threshold(
    y_test, 
    rf_baseline.predict_proba(X_test_scaled)[:, 1]
)

print(f"Optimal threshold: {optimal_threshold:.4f}")
print(f"Minimum cost: ${min_cost:,.2f}")

# Apply optimal threshold
y_pred_optimized = (rf_baseline.predict_proba(X_test_scaled)[:, 1] >= optimal_threshold).astype(int)
evaluate_fraud_model(y_test, y_pred_optimized, 
                     rf_baseline.predict_proba(X_test_scaled)[:, 1], 
                     "Random Forest (Optimized Threshold)")

Stacking Multiple Anomaly Detectors

Combine predictions from multiple algorithms:

from sklearn.ensemble import StackingClassifier
from sklearn.linear_model import LogisticRegression

# Create meta-features from anomaly scores
X_train_meta = np.column_stack([
    iforest.score_samples(X_train_scaled),
    lof.score_samples(X_train_scaled),
    rf_baseline.predict_proba(X_train_scaled)[:, 1]
])

X_test_meta = np.column_stack([
    iforest.score_samples(X_test_scaled),
    lof.score_samples(X_test_scaled),
    rf_baseline.predict_proba(X_test_scaled)[:, 1]
])

# Train meta-classifier
meta_clf = LogisticRegression(class_weight='balanced', random_state=42)
meta_clf.fit(X_train_meta, y_train)

y_pred_ensemble = meta_clf.predict(X_test_meta)

evaluate_fraud_model(y_test, y_pred_ensemble, 
                     meta_clf.predict_proba(X_test_meta)[:, 1], 
                     "Ensemble (Stacked Models)")

Feature Importance and Model Interpretability

Understanding which features drive fraud predictions is crucial for business stakeholders:

# Extract feature importance from Random Forest
feature_importance = pd.DataFrame({
    'feature': X.columns,
    'importance': rf_baseline.feature_importances_
}).sort_values('importance', ascending=False)

# Plot top 15 features
plt.figure(figsize=(10, 8))
plt.barh(range(15), feature_importance['importance'].head(15))
plt.yticks(range(15), feature_importance['feature'].head(15))
plt.xlabel('Importance Score')
plt.title('Top 15 Features for Fraud Detection')
plt.gca().invert_yaxis()
plt.tight_layout()
plt.show()

# Analyze fraud patterns in top features
top_features = feature_importance['feature'].head(5).tolist()

fig, axes = plt.subplots(2, 3, figsize=(15, 8))
axes = axes.ravel()

for idx, feature in enumerate(top_features):
    df[df['Class'] == 0][feature].hist(bins=50, alpha=0.5, label='Legitimate', ax=axes[idx])
    df[df['Class'] == 1][feature].hist(bins=50, alpha=0.5, label='Fraud', ax=axes[idx])
    axes[idx].set_title(f'{feature} Distribution')
    axes[idx].legend()
    axes[idx].set_xlabel(feature)

plt.tight_layout()
plt.show()

Real-Time Fraud Detection Pipeline

In production, fraud detection systems must operate in real-time:

import joblib
from datetime import datetime

class FraudDetectionPipeline:
    """
    Production-ready fraud detection pipeline
    """
    def __init__(self, model_path='fraud_model.pkl', threshold=0.5):
        self.model = joblib.load(model_path)
        self.scaler = StandardScaler()
        self.threshold = threshold

    def preprocess(self, transaction_data):
        """
        Preprocess incoming transaction
        """
        # Extract features
        features = transaction_data[['Time', 'Amount'] + [f'V{i}' for i in range(1, 29)]]

        # Scale features
        features_scaled = self.scaler.transform(features.values.reshape(1, -1))

        return features_scaled

    def predict(self, transaction_data):
        """
        Predict fraud probability for a single transaction
        """
        # Preprocess
        features = self.preprocess(transaction_data)

        # Get fraud probability
        fraud_prob = self.model.predict_proba(features)[0, 1]

        # Make decision
        is_fraud = fraud_prob >= self.threshold

        return {
            'transaction_id': transaction_data.get('transaction_id'),
            'fraud_probability': float(fraud_prob),
            'is_fraud': bool(is_fraud),
            'timestamp': datetime.now().isoformat(),
            'decision': 'BLOCK' if is_fraud else 'APPROVE'
        }

    def explain_prediction(self, transaction_data, top_n=5):
        """
        Provide feature-level explanation for prediction
        """
        features = self.preprocess(transaction_data)
        prediction = self.predict(transaction_data)

        # Get feature contributions (simplified - use SHAP in production)
        feature_names = ['Time', 'Amount'] + [f'V{i}' for i in range(1, 29)]
        feature_values = features[0]

        explanation = {
            **prediction,
            'top_contributing_features': [
                {'feature': name, 'value': float(value)}
                for name, value in sorted(
                    zip(feature_names, feature_values),
                    key=lambda x: abs(x[1]),
                    reverse=True
                )[:top_n]
            ]
        }

        return explanation

# Example usage
# pipeline = FraudDetectionPipeline(model_path='fraud_rf_model.pkl', threshold=optimal_threshold)
# result = pipeline.predict(new_transaction)
# print(result)

Production Considerations

When deploying fraud detection systems:

Aspect Recommendation
Latency Models must score transactions in <100ms; use model compression, feature selection
Model Drift Monitor fraud patterns monthly; retrain when performance degrades >5%
False Positives High FP rate causes customer friction; optimize threshold to balance fraud loss vs customer experience
Explainability Use SHAP or LIME to explain decisions for regulatory compliance
Data Privacy Implement PCA or encryption for sensitive features
A/B Testing Test new models on 10% traffic before full rollout
Feedback Loop Collect fraud investigation results to improve training data

Conclusion

In this episode, we’ve explored the challenging problem of financial fraud detection using anomaly detection techniques. We’ve covered:

  • Understanding class imbalance and its impact on model performance
  • Resampling techniques: undersampling, oversampling, and SMOTE
  • Anomaly detection algorithms: Isolation Forest, LOF, and One-Class SVM
  • Proper evaluation metrics for imbalanced classification: precision, recall, F1, ROC-AUC, and PR-AUC
  • Advanced techniques: cost-sensitive learning, threshold optimization, and model ensembles
  • Building production-ready fraud detection pipelines

The key takeaway: fraud detection requires domain-specific evaluation metrics and techniques beyond standard classification approaches. Always optimize for business metrics (fraud loss vs false alarm costs) rather than generic accuracy scores.

In our final episode, we’ll explore Deep Learning for Algorithmic Trading, covering LSTM networks for sequence modeling, Transformer architectures for market prediction, and reinforcement learning for portfolio optimization. We’ll bring together everything we’ve learned about financial time-series, feature engineering, and model evaluation to build sophisticated trading strategies.

Happy fraud hunting! 🔍

Mastering Financial Data Science with Kaggle Series (5/6)

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