Engineeering & IT Projects and Resources: Anomaly Detection in Financial Transactions

Anomaly Detection in Financial Transactions

1. Introduction

Objective: Develop a system to detect anomalies in financial transactions using machine learning techniques such as Isolation Forest or Autoencoders.
Purpose: Ensure early detection of fraudulent or unusual activities in financial data to enhance security and operational efficiency.

2. Project Workflow

1. Problem Definition:
   - Detect irregularities in financial transactions that may indicate fraud.
   - Challenges include imbalanced data and evolving fraud patterns.
2. Data Collection:
   - Source: Transaction logs, synthetic datasets (e.g., Kaggle financial datasets).
3. Data Preprocessing:
   - Handle missing values, normalize features, and encode categorical variables.
4. Model Selection:
   - Unsupervised approach: Isolation Forest or Autoencoders.
5. Evaluation:
   - Use metrics like Precision, Recall, and AUC-ROC.

3. Technical Requirements

- Programming Language: Python
- Libraries/Tools:
- Machine Learning: Scikit-learn, TensorFlow, Keras, PyTorch
- Data Handling: Pandas, NumPy
- Visualization: Matplotlib, Seaborn

4. Implementation Steps

Step 1: Setup Environment

Install required libraries:
```
pip install pandas numpy matplotlib seaborn scikit-learn tensorflow keras
```

Step 2: Load and Preprocess Data

Load the dataset:
```
import pandas as pd

data = pd.read_csv("financial_transactions.csv")
print(data.head())
```
Preprocess the data:
```
from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
data_scaled = scaler.fit_transform(data.drop(columns=['TransactionID', 'Label']))
```

Step 3: Anomaly Detection Using Isolation Forest

Train and evaluate Isolation Forest:
```
from sklearn.ensemble import IsolationForest

isolation_forest = IsolationForest(contamination=0.01)
isolation_forest.fit(data_scaled)

data['anomaly_score'] = isolation_forest.decision_function(data_scaled)
data['anomaly'] = isolation_forest.predict(data_scaled)
```

Step 4: Anomaly Detection Using Autoencoders

Train and evaluate an Autoencoder:
```
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

# Define Autoencoder model
autoencoder = Sequential([
    Dense(32, activation='relu', input_dim=data_scaled.shape[1]),
    Dense(16, activation='relu'),
    Dense(8, activation='relu'),
    Dense(16, activation='relu'),
    Dense(32, activation='relu'),
    Dense(data_scaled.shape[1], activation='sigmoid')
])

autoencoder.compile(optimizer='adam', loss='mse')
autoencoder.fit(data_scaled, data_scaled, epochs=50, batch_size=32, shuffle=True)

# Reconstruction error
reconstructions = autoencoder.predict(data_scaled)
mse = np.mean(np.power(data_scaled - reconstructions, 2), axis=1)
threshold = np.percentile(mse, 99)
data['anomaly'] = mse > threshold
```

Step 5: Evaluation

Evaluate performance:
```
from sklearn.metrics import classification_report, roc_auc_score

print(classification_report(data['Label'], data['anomaly']))
auc = roc_auc_score(data['Label'], data['anomaly_score'])
print("AUC-ROC:", auc)
```

5. Expected Outcomes

1. A system capable of identifying anomalies in financial transactions.
2. Improved understanding of how Isolation Forest and Autoencoders perform on financial data.
3. Visualization of anomaly patterns for better interpretability.

6. Additional Suggestions

- Fine-tune the contamination parameter in Isolation Forest for better results.
- Experiment with variational autoencoders for enhanced anomaly detection capabilities.
- Incorporate real-time detection capabilities with API integrations.

Pages