Github Link: https://github.

com/ManojS13-03/Data-science-

Project Title: Guarding transaction with AI-powered credit fraud detection

and prevention

PHASE-2

1. Problem Statement

In today’s increasingly digital financial ecosystem, credit card fraud has become a growing
threat, resulting in significant financial losses for individuals, businesses, and financial
institutions. Traditional fraud detection methods, which rely heavily on static rule-based systems
and manual reviews, are often insufficient to keep pace with the evolving tactics of
cybercriminals. These methods typically fail to detect sophisticated fraud patterns in real time,
leading to delayed responses and compromised user trust.

The challenge lies in developing an intelligent, real-time system capable of accurately

identifying and preventing fraudulent credit transactions without disrupting legitimate customer
activity. Such a system must efficiently process massive volumes of transaction data, detect
anomalies, adapt to new fraud patterns, and minimize false positives.

This problem necessitates the use of advanced AI techniques—including machine learning,

anomaly detection, and behavioral analytics—to enhance fraud detection capabilities and ensure
the security and integrity of financial transactions in a scalable, efficient, and user-friendly
manner.

2. Project Objectives

Design and implement machine learning models capable of detecting fraudulent credit
card transactions with high accuracy, leveraging both supervised and unsupervised
learning techniques.

Build a system that can monitor and analyze credit card transactions in real time to
instantly flag suspicious activity and prevent fraudulent transactions before completion.

Optimize the model to reduce the number of legitimate transactions mistakenly

flagged as fraudulent, thereby improving customer satisfaction and operational
efficiency.
 Implement adaptive learning mechanisms to allow the system to evolve
continuously and stay ahead of new fraud tactics and techniques.

Ensure that all data handling complies with relevant regulations (e.g., PCI DSS,
GDPR) and incorporates robust encryption and anonymization protocols to protect user
information.

Develop the fraud detection system to be easily deployable across various

platforms and capable of handling large transaction volumes without performance
degradation.

Incorporate explainable AI (XAI) components to offer insights into how fraud

decisions are made, and generate detailed reports for analysts and stakeholders.

3. Flowchart of the Project Workflow

4. Data Description

 Transaction ID: Unique identifier for each transaction

 Timestamp: Date and time of the transaction
 Transaction Amount: The value of the transaction
 Merchant Details: Merchant name, category, and location
 Payment Method: Type of card used (credit/debit), chip/swipe/online
 Currency: Currency used in the transaction
 Transaction Status: Approved, declined, or pending
  Label (Fraud/Legit): Indicates whether the transaction was fraudulent (for supervised
learning)

 User ID: Unique identifier for each user

 Age / Gender / Location: Basic demographics (when available)
 Account Tenure: How long the user has had the account
 Typical Spending Patterns: Average transaction value, frequency
 Login IP and Device Data: Used to detect location or device anomalies
 Previous Fraud Flags: If the account has been compromised before

 Time of Day for Transactions

 Geolocation Consistency: Are locations changing rapidly or unexpectedly?
 Device Fingerprinting: Are new or unfamiliar devices being used?
 Velocity Checks: Rapid transactions in a short time fram

 Blacklisted IPs and Merchants

 Known Fraud Patterns or Threat Intelligence Feeds
 Geopolitical Data: Regions with higher fraud risk
 Exchange Rates / Market Trends (for financial context)

 Ground truth labels: Fraudulent (1) vs. Legitimate (0) transactions

 May be obtained from chargeback data, manual analyst reviews, or law enforcement
reports
 DATASET LINK: https://www.kaggle.com/datasets/ayushvarshnay/credit-card-fraud-
detection-dataset/data

5. Data Preprocessing

 Remove duplicates: Eliminate repeated transactions or logs.

 Handle missing values:

 Impute missing values using mean, median, or mode (for numeric features).
 Drop irrelevant or sparsely populated features if necessary.

 Correct data types: Ensure date fields, amounts, and categorical data are in the correct
format.

 Filter out irrelevant data: Exclude transactions outside the project scope (e.g., non-card-
based payments if irrelevant).

One-Hot Encoding: For merchant type, device type, etc.

6. Exploratory Data Analysis (EDA)

Analyze individual features to spot trends and outliers.

 Transaction Amount
o Distribution of amounts for fraud vs. legitimate
o Fraud transactions often cluster at high or low extremes
o Plot: Histograms, box plots (separated by fraud flag)
 Transaction Time
o Peak hours or days for fraud activity
o Fraud may spike during non-business hours
 Merchant Category / Location
o Top categories or countries where fraud is most frequent
o Plot: Bar charts

BIVARIATE ANALYSIS

Explore relationships between features and the fraud label.

 Amount vs. Fraud

o Scatter plot or KDE to compare transaction amount patterns
 Transaction Time vs. Fraud
o Heatmaps or line plots showing time-based fraud frequency
 User Behavior
o Number of transactions per user
o Fraudulent users may have burst activity or unusual velocity

CORELLATION ANALYSIS

 Use df.corr() and a heatmap to identify highly correlated numerical features.

 This helps detect multicollinearity and understand relationships.

 Categorical Features:
o Use pivot tables or groupby to calculate fraud rates by:
 Payment type
 Device used
 Merchant category
o Plot: Bar plots showing fraud rate per category
 Geospatial Analysis (if location data is available):
o Map fraud hotspots by region/country
o Identify location mismatches between user and transaction

7. Feature Engineering
TRANSACTION BASED FEATURES

 Transaction Amount:
o The amount of the transaction is a fundamental indicator. Larger or smaller-than-
usual transactions might indicate fraud.
o New Feature: Log transformation to reduce the effect of outliers.
 Transaction Time:
o Hour of Transaction: Fraud often happens at unusual hours (late night or early
morning).
o Day of Week: Fraud rates may vary by day of the week, with weekends or
holidays showing a spike.
o Time Since Last Transaction: Large gaps between transactions or multiple
transactions in a short time frame may indicate fraudulent activity.
 Merchant Information:
o Merchant Category: Certain merchant types may be more prone to fraud (e.g.,
online retailers).
o Merchant Location: A transaction from a different region or country than usual
could raise a flag.
 Transaction Frequency (Velocity):
o Transaction Count: Number of transactions within a specific time window (e.g.,
1 hour, 24 hours).
o Average Transaction Amount: Average amount spent over the past few
transactions.
o Rapid Transaction Sequences: If multiple transactions occur within a short
timeframe, this may be flagged.

USER BASED FEATURES

These features focus on the individual user’s behaviors and historical patterns.

 User’s Transaction History:

o Average Transaction Amount: Mean transaction value for a given user,
normalized by time period.
o Total Spend: Total amount spent in the last N days or months.
o Spend Deviation: How much current transaction deviates from user’s typical
spending habits.
 Behavioral Consistency:
o Geolocation Consistency: Frequency of location mismatch between user and
transaction.
o Device Fingerprinting: Number of different devices used by the same user in
recent transactions.
o Login Patterns: Number of times the user logs in within a specific time window.
 Account Tenure:
o Account Age: How long the account has been active. Fraud may be more
common on newly created accounts.
 ●

8. Model Building

1.Data Preparation

Before building the model, ensure your data is properly preprocessed. This includes:

 Feature Engineering: As described earlier, create meaningful features.

 Data Splitting: Split the data into training, validation, and test sets.
o Typically, 70-80% for training, 10-15% for validation, and 10-15% for testing.
o Ensure there is no data leakage by splitting based on time or transaction sequence
when necessary.

2.Choosing Algorithms

Credit fraud detection is typically a binary classification problem (fraud or legitimate). Here
are common algorithms for this task:

 Logistic Regression: A simple, interpretable model that can be a good baseline.

 Random Forest: A robust ensemble method that handles imbalanced data well and
provides feature importance insights.
 Gradient Boosting Machines (GBM) (e.g., XGBoost, LightGBM, CatBoost): These
powerful models are often the top performer for fraud detection tasks due to their ability
to handle non-linear relationships and interactions between features.
 Neural Networks: Deep learning models can be effective for large datasets but may
require more computational resources.
 Support Vector Machines (SVM): Can be used for binary classification, especially in
high-dimensional feature spaces.

9. Visualization of Results & Model Insights

Confusion Matrix

A Confusion Matrix gives a clear picture of how the model performs by showing the number of
true positives, true negatives, false positives, and false negatives. It is essential for understanding
the performance on imbalanced datasets.
Classification Report

The Classification Report provides important metrics such as precision, recall, F1-score, and
support for both classes (fraud and legitimate). These metrics help evaluate the effectiveness of
the model in detecting fraudulent transactions

ROC Curve and AUC (Area Under the Curve)

The ROC Curve is a graphical representation of the model’s performance at all classification
thresholds. The AUC (Area Under the Curve) score gives an aggregate measure of the model’s
performance, with higher values indicating better performance. The AUC-ROC curve is
particularly helpful for imbalanced datasets.

10. Tools and Technologies Used

● Programming Language: Python 3

● Notebook Environment: Google Colab
● Key Libraries:
○ pandas, numpy for data handling
○ matplotlib, seaborn, plotly for visualizations
○ scikit-learn for preprocessing and modeling
○ Gradio for interface deployment

11. Team Members and Contributions

1. S. Manoj – Team Lead & Data Acquisition and Integration

Role:
Lead the collection and integration of diverse datasets required for training and evaluating fraud
detection models.

Key Responsibilities:

 Acquire anonymized transaction data, including transaction amounts, merchant details,

timestamps, and locations.
 Collect behavioral data such as user login times, device usage patterns, and transaction
frequencies.
 Integrate external data sources (e.g., IP geolocation, device fingerprinting, historical
fraud records) to enrich datasets.
 Ensure adherence to data privacy and protection regulations (e.g., GDPR, CCPA) during
data handling processes.
🔹 2. J. Mohamed Javith – Data Preprocessing & Feature Engineering

Role:
Transform raw data into a structured format and engineer meaningful features to enhance model
accuracy and reliability.

Key Responsibilities:

 Clean the data by resolving missing values, removing outliers, and eliminating duplicates.
 Normalize and standardize datasets to ensure uniformity and consistency across data
sources.
 Engineer domain-specific features that reflect transaction behavior, user profiles, and
contextual fraud signals.
 Apply methods to handle class imbalance (e.g., SMOTE, random undersampling) to
improve model learning.

🔹 3. M. Muthu – Model Development & Training

Role:
Design and train machine learning and deep learning models tailored for credit fraud detection.

Key Responsibilities:

 Choose appropriate algorithms (e.g., Random Forest, Support Vector Machines,

Autoencoders) for both supervised and unsupervised learning tasks.
 Train models using prepared data and evaluate them with robust metrics such as
accuracy, precision, recall, and F1-score.
 Perform hyperparameter tuning to improve model generalization and minimize
overfitting.
 Continuously test model robustness against new fraud patterns.

🔹 4. M. Nithish Kumar – Real-Time System Integration & Compliance

Role:
Deploy the trained models into a real-time detection system while ensuring compliance with
industry regulations.

Key Responsibilities:
 Develop and maintain the system architecture to support real-time fraud analysis during
transaction processing.
 Seamlessly integrate models into live transaction pipelines for instant decision-making.
 Implement automated alert mechanisms for suspicious transactions.
 Ensure full compliance with regulatory frameworks such as PCI DSS, GDPR, and other
relevant standards.
 Maintain secure audit logs and generate periodic compliance and performance reports.