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. 

Preparing a Dataset for Fine-Tuning Foundation Model

 

I am trying to preparing a Dataset for Fine-Tuning on Pathology Lab Data.

1. Dataset Collection

   - Sources: Gather data from pathology lab reports, medical journals, and any other relevant medical documents.

   - Format: Ensure that the data is in a readable format like CSV, JSON, or text files.

2. Data Preprocessing

   - Cleaning: Remove any irrelevant data, correct typos, and handle missing values.

   - Formatting: Convert the data into a format suitable for fine-tuning, usually pairs of input and output texts.

   - Example Format:

     - Input: "Patient exhibits symptoms of hyperglycemia."

     - Output: "Hyperglycemia"

3. Tokenization

   - Tokenize the text using the tokenizer that corresponds to the model you intend to fine-tune.

Example Code for Dataset Preparation

Using Pandas and Transformers for Preprocessing

1. Install Required Libraries:

   ```sh

   pip install pandas transformers datasets

   ```

2. Load and Clean the Data:

   ```python

   import pandas as pd

   # Load your dataset

   df = pd.read_csv("pathology_lab_data.csv")

   # Example: Remove rows with missing values

   df.dropna(inplace=True)

   # Select relevant columns (e.g., 'report' and 'diagnosis')

   df = df[['report', 'diagnosis']]

   ```

3. Tokenize the Data:

   ```python

   from transformers import AutoTokenizer

   model_name = "pretrained_model_name"

   tokenizer = AutoTokenizer.from_pretrained(model_name)

   def tokenize_function(examples):

       return tokenizer(examples['report'], padding="max_length", truncation=True)

   tokenized_dataset = df.apply(lambda x: tokenize_function(x), axis=1)

   ```

4. Convert Data to HuggingFace Dataset Format:

   ```python

   from datasets import Dataset

   dataset = Dataset.from_pandas(df)

   tokenized_dataset = dataset.map(tokenize_function, batched=True)

   ```

5. Save the Tokenized Dataset:

   ```python

   tokenized_dataset.save_to_disk("path_to_save_tokenized_dataset")

   ```

Example Pathology Lab Data Preparation Script

Here is a complete script to prepare pathology lab data for fine-tuning:

```python

import pandas as pd

from transformers import AutoTokenizer

from datasets import Dataset

# Load your dataset

df = pd.read_csv("pathology_lab_data.csv")

# Clean the dataset (remove rows with missing values)

df.dropna(inplace=True)

# Select relevant columns (e.g., 'report' and 'diagnosis')

df = df[['report', 'diagnosis']]

# Initialize the tokenizer

model_name = "pretrained_model_name"

tokenizer = AutoTokenizer.from_pretrained(model_name)

# Tokenize the data

def tokenize_function(examples):

    return tokenizer(examples['report'], padding="max_length", truncation=True)

dataset = Dataset.from_pandas(df)

tokenized_dataset = dataset.map(tokenize_function, batched=True)

# Save the tokenized dataset

tokenized_dataset.save_to_disk("path_to_save_tokenized_dataset")

```

Notes

- Handling Imbalanced Data: If your dataset is imbalanced (e.g., more reports for certain diagnoses), consider techniques like oversampling, undersampling, or weighted loss functions during fine-tuning.

- Data Augmentation: You may also use data augmentation techniques to artificially increase the size of your dataset.

By following these steps, you'll have a clean, tokenized dataset ready for fine-tuning a model on pathology lab data.

You can read my other article about data preparation. 

PySpark Why and When to Use

 

PySpark and pandas are both popular tools in the data science and analytics world, but they serve different purposes and are suited for different scenarios. Here's when and why you might choose PySpark over pandas:

1. Big Data Handling:

   - PySpark: PySpark is designed for distributed data processing and is particularly well-suited for handling large-scale datasets. It can efficiently process data stored in distributed storage systems like Hadoop HDFS or cloud-based storage. PySpark's capabilities shine when dealing with terabytes or petabytes of data that would be impractical to handle with pandas.

   - pandas: pandas is ideal for working with smaller datasets that can fit into memory on a single machine. While pandas can handle reasonably large datasets, their performance might degrade when dealing with very large data due to memory constraints.

2. Parallel and Distributed Processing:

   - PySpark: PySpark performs distributed processing by leveraging the power of a cluster of machines. It can parallelize operations and distribute tasks across nodes in the cluster, resulting in efficient processing of large-scale data.

   - pandas: pandas operates on a single machine, utilizing only one core. This limits its parallel processing capabilities, making it less suitable for distributed processing of large datasets.

3. Data Processing Speed:

   - PySpark: For large datasets, PySpark's distributed processing capabilities can lead to faster data processing compared to pandas. It can take advantage of the parallelism offered by clusters, resulting in improved performance.

   - pandas: pandas is fast for processing small to medium-sized datasets, but it might slow down for large datasets due to memory constraints and single-core processing.

4. Ease of Use and Expressiveness:

   - PySpark: PySpark's API is designed to be familiar to those who are already comfortable with Python and pandas. However, due to its distributed nature, some operations might require a different mindset and involve additional steps.

   - pandas: pandas provides an intuitive and user-friendly API for data manipulation and analysis. Its syntax is often considered more expressive and easier to work with for small to medium-sized datasets.

5. Ecosystem and Libraries:

   - PySpark: PySpark integrates well with other components of the Apache Spark ecosystem, such as Spark SQL, MLlib for machine learning, and GraphX for graph processing. It's a good choice when you need a unified platform for various data processing tasks.

   - pandas: pandas has a rich ecosystem of libraries and tools that complement its functionality, including NumPy for numerical computations, scikit-learn for machine learning, and Matplotlib for data visualization.

In summary, use PySpark when you're dealing with big data and need distributed processing capabilities, especially when working with clusters and distributed storage systems. Use pandas when working with smaller datasets that can fit into memory on a single machine and when you need a more user-friendly and expressive API for data manipulation and analysis.

Sure, let's take a look at some code examples to compare PySpark and pandas, as well as how Spark SQL can be helpful.

Example 1: Data Loading and Filtering

Suppose you have a CSV file containing a large amount of data, and you want to load the data and filter it based on certain conditions.

Using pandas:

```python

import pandas as pd

# Load data

df = pd.read_csv('data.csv')

# Filter data

filtered_data = df[df['age'] > 25]

```

Using PySpark:

```python

from pyspark.sql import SparkSession

# Create a Spark session

spark = SparkSession.builder.appName('example').getOrCreate()

# Load data as a DataFrame

df = spark.read.csv('data.csv', header=True, inferSchema=True)

# Filter data using Spark SQL

filtered_data = df.filter(df['age'] > 25)

```

Example 2: Aggregation

Let's consider an example where you want to calculate the average salary of employees by department.

Using pandas:

```python

import pandas as pd

# Load data

df = pd.read_csv('data.csv')

# Calculate average salary by department

avg_salary = df.groupby('department')['salary'].mean()

```

Using PySpark:

```python

from pyspark.sql import SparkSession

# Create a Spark session

spark = SparkSession.builder.appName('example').getOrCreate()

# Load data as a DataFrame

df = spark.read.csv('data.csv', header=True, inferSchema=True)

# Calculate average salary using Spark SQL

df.createOrReplaceTempView('employee')

avg_salary = spark.sql('SELECT department, AVG(salary) AS avg_salary FROM employee GROUP BY department')

```

How Spark SQL Helps:

Spark SQL is a component of PySpark that allows you to run SQL-like queries on your distributed data. It provides the following benefits:

1. Familiar Syntax: If you're already familiar with SQL, you can leverage your SQL skills to query and manipulate data in PySpark.

2. Performance Optimization: Spark SQL can optimize your queries for distributed execution, leading to efficient processing across a cluster of machines.

3. Integration with DataFrame API: Spark SQL seamlessly integrates with the DataFrame API in PySpark. You can switch between DataFrame operations and SQL queries based on your preferences and requirements.

4. Hive Integration: Spark SQL supports querying data stored in Hive tables, making it easy to work with structured data in a distributed manner.

5. Compatibility: Spark SQL supports various data sources, including Parquet, Avro, ORC, JSON, and more.

In summary, while pandas is great for working with smaller datasets on a single machine, PySpark's distributed processing capabilities make it suitable for big data scenarios. Spark SQL enhances PySpark by allowing you to use SQL-like queries for data manipulation and analysis, optimizing performance for distributed processing.

Photo by Viktoria