Comparative Analysis of Various Online Fraud Detection Techniques and their Challenges

Abstract

Online fraudulent transactions are a
significant criminal violation. People and
financial institutions lose billions of dollars
every year. It highlights the importance of
financial institutions in detecting and
preventing fraud. Machine Learning
algorithms provide aprocess that can prevent
online business fraud with high accuracy.
Online fraud poses a significant threat to ecommerce and financial services. This study
explores the application of supervised
learning techniques for online fraud
detection. The primary objective is to build a
robust and accurate system that can
distinguish between legitimate and fraudulent
transactions in real-time. To achieve this goal,
we collect historical transaction data,
including relevant featuressuch as transaction
amount, location, time, and user behaviour.
This data is meticulously pre-processed to
handle missing values, outliers, and data
quality issues. Feature engineering is
performed to create new variables that
enhance the model's predictive power.
The dataset is divided into a training set; test
set and test set to facilitate the design process.
Various supervised learning algorithms
included logistic regression, decision tree;
random forest and support vector machines
consider finding a lie. The selected model is
trained on the labelled examples in the
training set, learning to discriminate between
fraudulent and legitimate transactions.
Perform hyperparameter tuning to optimize
model performance and Chapter F1 results.
When the model's performance meets
predefined criteria, it is sent to: The online
system allows it to record new changes in realtime.