Credit Card Fraud Detection with Machine Learning Classifiers
Abstract
A comparative study that flags fraudulent card transactions in real time, training Logistic Regression, Decision Tree, Random Forest, and Neural Network models with balanced data preparation and a privacy-safe prediction flow.
Introduction
Credit card fraud costs banks, shops, and everyday customers a huge amount of money every year. As more of us pay online, fraudsters keep finding new ways to misuse stolen card details, often before anyone notices something is wrong.
Checking every payment by hand is simply impossible at today's scale. This is where smart software helps: it learns what normal spending looks like and quietly flags anything that seems out of place, so suspicious transactions can be caught early.
Problem Statement
Spotting fraud is trickier than it sounds:
- Fraud is rare: so it is easy to overlook among millions of genuine payments.
- Fraud keeps changing: so fixed rules quickly fall behind.
- Decisions have to be made almost instantly: not after the fact.
- Sensitive card details must stay private throughout the whole process.
Objectives
- Catch fraudulent transactions quickly and reliably.
- Learn from real spending patterns instead of rigid, manual rules.
- Compare a few trusted approaches to see which fits different needs.
- Keep the solution affordable and easy to run for a business of any size.
- Offer a simple way to check a transaction without ever exposing raw card data.
Dataset
The work is built around a dataset that fits the problem well. Depending on what a client needs, this can be a public benchmark, licensed or private records, data from other trusted sources, or a custom set put together for the project. Whatever the source, the data is cleaned and prepared before any modelling begins, and personal details are kept protected.
Methodology
The project trains and compares four models on the same prepared data: Logistic Regression, Decision Tree, Random Forest, and an Artificial Neural Network (ANN). Before training, the data is cleaned and balanced so rare fraud cases are not lost, then fairly split for testing.
What makes the approach stand out is the honest, side-by-side view it gives. Rather than chasing a single "best" model, it weighs simple, transparent methods against more powerful ones, so a business can choose based on what matters most to them: speed, clarity, or raw detection strength. Because every model is prepared and tested the same way, the comparison stays fair and the outcome is easy to trust.