Real Time Fraud Detection with Generative AI

 

Photo by Mikhail Nilov in pexel

Fraud detection is a critical task in various industries, including finance, e-commerce, and healthcare. Generative AI can be used to identify patterns in data that indicate fraudulent activity.

Tools and Libraries:

Python: Programming language
TensorFlow or PyTorch: Deep learning frameworks
Scikit-learn: Machine learning library
Pandas: Data manipulation library
NumPy: Numerical computing library
Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs): Generative AI models

Code:

Here's a high-level example of how you can use GANs for real-time fraud detection:


Data Preprocessing:

import pandas as pd
from sklearn.preprocessing import StandardScaler
# Load data
data = pd.read_csv('fraud_data.csv')
# Preprocess data
scaler = StandardScaler()
data_scaled = scaler.fit_transform(data)


GAN Model:

import tensorflow as tf
from tensorflow.keras.layers import Input, Dense, Reshape, Flatten
from tensorflow.keras.layers import BatchNormalization, LeakyReLU
from tensorflow.keras.models import Sequential
# Define generator and discriminator models
generator = Sequential([
    Dense(64, input_shape=(100,)),
    LeakyReLU(),
    BatchNormalization(),
    Dense(128),
    LeakyReLU(),
    BatchNormalization(),
    Dense(256),
    LeakyReLU(),
    BatchNormalization(),
    Dense(784, activation='tanh')
])
discriminator = Sequential([
    Dense(64, input_shape=(784,)),
    LeakyReLU(),
    BatchNormalization(),
    Dense(128),
    LeakyReLU(),
    BatchNormalization(),
    Dense(256),
    LeakyReLU(),
    BatchNormalization(),
    Dense(1, activation='sigmoid')
])
# Compile GAN model
gan = tf.keras.models.Sequential([generator, discriminator])
gan.compile(loss='binary_crossentropy', optimizer='adam')


Training:

# Train GAN model
gan.fit(data_scaled, epochs=100, batch_size=32)
Real-time Fraud Detection:
Python
# Define a function to detect fraud in real-time
def detect_fraud(data_point):
    # Generate a synthetic data point using the generator
    synthetic_data_point = generator.predict(data_point)
    
    # Calculate the discriminator score
    discriminator_score = discriminator.predict(synthetic_data_point)
    
    # If the score is below a threshold, classify as fraud
    if discriminator_score < 0.5:
        return 1
    else:
        return 0
# Test the function
data_point = pd.read_csv('new_data_point.csv')
fraud_detected = detect_fraud(data_point)
print(fraud_detected)


Note: This is a simplified example and may need to be adapted to your specific use case. Additionally, you may need to fine-tune the model and experiment with different architectures and hyperparameters to achieve optimal results.

You can contact me for a guide on how to learn more about the real use case. Thank you.