this is a ppt for fraud detection in online transaction .

1. Fraud Detection
in Online
Transactions
ARYAN CHATURVEDI – 23SCSE1180472

2. Introduction
CONTEXT AND MOTIVATION
Online payments are increasingly popular, leading to a
surge in fraud cases. Machine learning offers a
promising solution for effective detection.

3. Problem Statement
CHALLENGES DRIVING OUR APPROACH
Fraudulent transactions lead to significant financial
losses, while manual detection methods are
ineffective and slow, necessitating an automated fraud
detection solution.

4. Project Objective
GOALS OF THE FRAUD DETECTION PROJECT
The primary aims are to analyze transactions, identify
fraud patterns, and develop a machine learning model
for accurate fraud prediction.

5. Dataset Overview
DESCRIPTION OF THE DATA USED
The dataset consists of real-world online transaction
data, including transaction types, amounts, and
balances, with the target variable indicating fraud
status.

6. Transaction Types
CASH_IN
CASH_IN transactions involve depositing
funds into an account, and they are
generally considered low-risk, with minimal
fraud occurrences.
CASH_OUT
CASH_OUT transactions involve
withdrawing funds from an account, often
presenting higher risks, as they can be
exploited for fraudulent activities.
TRANSFER
TRANSFER transactions involve moving
funds between accounts, frequently
targeted by fraudsters due to the potential
for quick, anonymous withdrawals.

7. Feature Engineering
Steps NUMERICAL CONVERSION
Transaction types were converted into
numerical values to facilitate effective
analysis and enhance the performance of
the machine learning model.
FEATURE SELECTION
Important features were selected for
modeling while unnecessary columns were
removed, ensuring that only relevant data
was analyzed for fraud detection.

8. Machine Learning
Model
MODEL SELECTION CHARACTERISTICS
We selected the Decision Tree Classifier for its
interpretability and effectiveness. It excels in solving
classification problems like fraud detection efficiently.

9. Model Performance Summary
ACCURACY
The model achieved high accuracy,
demonstrating its effectiveness in
identifying fraudulent transactions in
online payments.
DETECTION SUCCESS
Fraudulent transactions were detected
successfully, showcasing the model's
capability to minimize financial losses in
digital transactions.
TEST PERFORMANCE
The model performed well on test data,
confirming its reliability and potential for
real-time fraud detection applications.

10. Conclusion
FUTURE DIRECTIONS FOR FRAUD DETECTION
The project demonstrates that Machine Learning
effectively detects fraud, and future enhancements
could involve deploying advanced models for
improved accuracy and real-time analysis.

11. Thank You for Your
Attention
EMAIL ADDRESS
aryan.chaturvedi@example.com
SOCIAL MEDIA
@aryan_18042
PHONE
7357737345