College Name: 8217-Sir Issac Newton College of Engineering

and Technology.
Department Name: Department of Artificial Intelligence and
Data Science.
Project Name: Fraud Detection in Financial Transactions.
Team Members:
Name Department Register Number
BALA S B.Tech Artificial 821722243007
Intelligence and Data
Science
DHANESH E B.Tech Artificial 821722243009
Intelligence and Data
Science
DHINESH M B.Tech Artificial 821722243012
Intelligence and Data
Science
GOKUL R B.Tech Artificial 821722243013
Intelligence and Data
Science
HARIKRISHNAN R B.Tech Artificial 821722243014
Intelligence and Data
Science
JOSUVA P M B.Tech Artificial 821722243020
Intelligence and Data
Science

Submitted By
 HARIKRISHNAN R
821722243014
Fraud Detection in Credit Card Transactions

Introduction:
Financial fraud continues to pose a significant threat, resulting in substantial financial
losses and undermining customer trust. This project aims to develop a robust system
using machine learning techniques for real-time detection of fraudulent transactions
in credit card usage.

Project Objectives:
1. Accuracy: Develop a highly accurate model capable of identifying fraudulent
transactions with minimal false positives.

2. Security Insights: Enhance security measures by analyzing evolving fraud

patterns.

3. Integration: Seamlessly integrate with existing transaction processing systems for

real-time fraud detection and flagging of suspicious activity.

System Requirements:
Data:
• Historical Transaction Data: A comprehensive dataset of historical
transactions, categorized as fraudulent or legitimate, should include:
• Customer Information: hashed or anonymized for privacy
• Transaction Details: Amount, location, time, merchant details
• Additional Features: Device type, IP address Hardware:

• Processing Power: Sufficient computing power, preferably with GPUs for deep
learning models (e.g., TensorFlow, PyTorch)
• Memory: Ample RAM to handle large datasets and complex algorithms

Software:
• Machine Learning Libraries: scikit-learn, TensorFlow, PyTorch
• Data Analysis Tools**: pandas, NumPy
• Development Environment: Jupyter Notebook
Methodology:
1. Data Preprocessing:
Data Acquisition and Exploration:
• Securely obtain historical transaction data.
• Explore the data to understand its structure, identify potential issues, and gain
insights into fraudulent patterns.

Data Cleaning:
• Address missing values using imputation techniques or domain-specific
knowledge.
• Handle outliers through capping, winsorization, or removal if they significantly
deviate from the normal range.
• Ensure data consistency by checking for formatting errors, invalid entries, and
inconsistencies between features.

Data Transformation:
• Encode categorical features using one-hot encoding or label encoding.
• Apply feature scaling (normalization or standardization) for
• Consider feature hashing for high-cardinality categorical features to reduce
dimensionality.

Feature Engineering:
• Transaction Features: Amount, frequency, time since last transaction, distance
from usual location.
• Customer Features: Average transaction amount, spending
habits, demographics.
• Merchant Features: Merchant category, location, historical fraud reports.
• Temporal Features: Day of week, time of day, month.
• Derived Features: Ratios, differences, statistical summaries.

2. Model Selection and Training:

Evaluation Criteria:
• Accuracy: Overall correctness
• Precision: Proportion of true positives
 • Recall: Proportion of identified fraud
• F1 Score: Harmonic mean of precision and recall
• Cost-Sensitive Metrics: Financial impact of misclassifications Algorithm

Selection:
Consider a range of machine learning algorithms suitable for fraud detection,
including Logistic Regression, Random Forest, Gradient Boosting Machines, and
Support Vector Machines.

3. Model Evaluation:
Evaluate the trained model's performance on the unseen testing set using metrics
such as:

• Accuracy: Percentage of correctly classified transactions.

• Precision: Proportion of flagged transactions that are truly fraudulent.
• Recall: Proportion of actual fraudulent transactions correctly identified.
• F1 Score: Harmonic mean of precision and recall.

4. Existing work:
• Rule-Based Systems: Set conditions trigger alerts for suspicious activity.
• Machine Learning Models: Algorithms analyze historical data for fraud
patterns.
• Anomaly Detection: Identifies unusual transactions compared to normal
behavior.
• Behavioral Analysis: Flags transactions deviating from typical spending
habits.
• Deep Learning: Neural networks learn complex patterns for fraud detection.

5. Proposed Work:
• Hybrid Models: Combine different techniques for stronger detection.
• Real-Time Processing: Detect fraud instantly for immediate action.
• Unsupervised Learning: Identify anomalies without needing labeled data.
• Feature Engineering: Improve model accuracy with new or refined features.
• Explainable AI: Make models easier to understand for users.
6. Flow Chart:

7. Implementation:
Program:
import pandas as pd from sklearn.model_selection
import train_test_split
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.compose import ColumnTransformer from
sklearn.pipeline import Pipeline from sklearn.ensemble import
RandomForestClassifier
from sklearn.metrics import classification_report, accuracy_score,
confusion_matrix import seaborn as sns import matplotlib.pyplot as
plt

# Load the dataset

data = pd.read_csv('/content/credit_card_transactions (4).csv') #
Replace with your actual file name

# Display basic information about the dataset

print(data.info()) print(data.describe())

# Check for missing values

print(data.isnull().sum())

# Plot the distribution of the classes

sns.countplot(x='Fraudulent', data=data)
plt.title('Class Distribution') plt.show()

# Separate features and target X =

data.drop(columns=['Fraudulent']) y =
data['Fraudulent']

# Preprocess the data

# We need to handle categorical variables: Transaction_Type and MCC
# Identify categorical features categorical_features
= ['Transaction_Type', 'MCC'] numerical_features =
['Transaction_ID']

# Create a column transformer with OneHotEncoder for categorical

features and StandardScaler for numerical features preprocessor
= ColumnTransformer( transformers=[
('num', StandardScaler(), numerical_features),
('cat', OneHotEncoder(), categorical_features)
])

# Create a pipeline that first transforms the data and then applies the
classifier pipeline = Pipeline(steps=[
('preprocessor', preprocessor),
('classifier', RandomForestClassifier(n_estimators=100, random_state=42))
])

# Split the data into training and testing sets

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,
random_state=42, stratify=y)

# Train the model

pipeline.fit(X_train, y_train)

# Make predictions on the test set y_pred

= pipeline.predict(X_test)

# Evaluate the model

print("Accuracy:", accuracy_score(y_test, y_pred)) print("Confusion
Matrix:\n", confusion_matrix(y_test, y_pred))
print("Classification Report:\n", classification_report(y_test,
y_pred))

# Plot the confusion matrix conf_matrix =

confusion_matrix(y_test, y_pred) sns.heatmap(conf_matrix,
annot=True, fmt="d", cmap="Blues") plt.title('Confusion
Matrix') plt.xlabel('Predicted') plt.ylabel('Actual')
plt.show()

Output:
Future Enhancements:
• Advanced Feature Engineering: Explore techniques like dimensionality
reduction (e.g., PCA).
• Deep Learning Models: Investigate recurrent neural networks (RNNs) or
convolutional neural networks (CNNs).
• Adaptive Learning: Implement models that adapt over time to new fraud
patterns.
• Explainable AI (XAI): Enhance model transparency for better decision-
making.
• Cost-Sensitive Optimization: Incorporate financial impact into the model's
learning process.

Conclusion:
This project successfully developed a machine learning-based system for detecting
fraudulent financial transactions. Through comprehensive data preprocessing, feature
engineering, and algorithm selection, the system demonstrates promising accuracy in
identifying potential fraud. Future enhancements will aim to further improve the
system's effectiveness and user trust, providing financial institutions with a valuable
tool to combat evolving fraud threats and protect their customers.