Fraud Detection algorithm using Deep linking

Key Concepts and Approach

• User Behavior Analysis:
  • Track detailed user interactions within an app using deep links.
  • Analyze patterns to distinguish between normal and suspicious behaviors.
• Transaction Monitoring:
  • Monitor transactions in real-time.
  • Use deep linking to trace the transaction journey and validate its legitimacy.
• Machine Learning Integration:
  • Use machine learning models to analyze data collected via deep links.
  • Train models to identify anomalies and predict fraudulent activities.

Implementation of algorithm

• Data Collection via Deep Links:
  1. Integrate deep links in the application to collect granular data on user actions.
  2. Store this data in a structured format for analysis.
• Feature Engineering:
  1. Extract relevant features from the collected data, such as click patterns, time spent on specific actions, and transaction paths.
  2. Normalize and preprocess the data for machine learning models.
• Machine Learning Model:
  1. Choose an appropriate machine learning algorithm (e.g., random forest, neural networks, or gradient boosting) for fraud detection.
  2. Train the model using historical data, including labeled instances of fraudulent and legitimate activities.
• Real-Time Fraud Detection:
  1. Implement the trained model in a real-time monitoring system.
  2. Use deep links to continuously feed data into the model for real-time fraud detection.

Step 1: Data Collection Simulation

1. Simulated user interaction and transaction data.
2. Features include click patterns, time spent, and transaction amounts.

import pandas as pd
import numpy as np

# Simulated dataset: user interactions and transactions
data = {
    'user_id': [1, 2, 3, 4, 5],
    'click_pattern': [np.random.randint(0, 10) for _ in range(5)], # Random click patterns
    'time_spent': [np.random.randint(100, 1000) for _ in range(5)], # Time spent in seconds
    'transaction_amount': [100, 250, 400, 5000, 175], # Transaction amounts
    'is_fraud': [0, 0, 0, 1, 0] # Labels: 1 for fraud, 0 for legitimate
}

df = pd.DataFrame(data)
print(df)

Step 2: Feature Engineering

1. Simplified feature extraction from click patterns.
2. Normalization of time spent and transaction amounts.

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

# Feature extraction: Flatten click patterns and normalize time spent and transaction amounts
df['click_pattern'] = df['click_pattern'].apply(lambda x: np.mean(x)) # Simplified feature extraction
X = df[['click_pattern', 'time_spent', 'transaction_amount']]
y = df['is_fraud']

# Train-test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Feature scaling
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

Step 3: Model Training

1. Random Forest classifier trained on the features.
2. Model evaluation using accuracy and classification report.

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

# Initialize and train the model
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)

# Make predictions
y_pred = model.predict(X_test)

# Evaluate the model
print("Accuracy:", accuracy_score(y_test, y_pred))
print(classification_report(y_test, y_pred))

Step 4: Real-Time Detection Simulation

1. Function to detect fraud using the trained model.
2. Simulated real-time detection of a new transaction.

# Function to detect fraud using the trained model
def detect_fraud(click_pattern, time_spent, transaction_amount):
    features = np.array([[click_pattern, time_spent, transaction_amount]])
    features = scaler.transform(features)
    prediction = model.predict(features)
    return "Fraud" if prediction == 1 else "Legitimate"

# Example usage
new_click_pattern = np.random.randint(0, 10)
new_time_spent = np.random.randint(100)
new_transaction_amount = 300
result = detect_fraud(new_click_pattern, new_time_spent, new_transaction_amount)
print("Transaction Status:", result)