Credit Card Fraud Detection with Machine Learning

Fraud detection, one of the many cases of anomaly detection is an important aspect of financial markets. Is there any way to predict whether a transaction is fraudulent or not based on the history of transactions? Let’s explore a neural network architecture as it attempts to predict the cases as frauds or not. By the end of this article, we’ll be able to build an encoder-decoder architecture from scratch using Keras and classify the transactions as fraudulent or non-fraudulent.

Dataset

We use a dataset credit card fraud detection by the ULB machine learning group. The dataset contains 28 anonymized variables, 1 “amount” variable, 1 “time” variable, and 1 target variable — Class. The variables are anonymized to protect the privacy of the customers as the dataset is in the public domain. The dataset can be found here. ‘0’ as target variable corresponds to the non-fraudulent cases whereas ‘1’ in target variable corresponds to fraudulent cases.

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Credit Card Fraud Detection via Machine Learning: A Case Study

This is the second and last part of my series which focuses on Anomaly Detection using Machine Learning. If you haven’t already, I recommend you read my first article here which will introduce you to Anomaly Detection and its applications in the business world.

In this article, I will take you through a case study focus on Credit Card Fraud Detection. It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase. So the main task is to identify fraudulent credit card transactions by using Machine learning. We are going to use a Python library called PyOD which is specifically developed for anomaly detection purposes.

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Credit card fraud and technical solutions

Credit card fraud is an increasingly expensive problem. Technology offers solutions to help combat the problem and gain control.
How to prevent fraudulent transactions in credit cards is a common question plaguing the credit card user today. The credit card brings convenience and security to the users, but the same can become a cause of agony if the user is a victim of any credit card fraud. Smart systems are coming to the aid of credit card users and empowering them against cybercriminals. Using fraud detection tools and following some simple precautions, the users can protect themselves against credit card fraud.

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Credit Card Fraud Detection with Python & Machine Learning

For any bank or financial organization, credit card fraud detection is of utmost importance. We have to spot potential fraud so that consumers can not bill for goods that they haven’t purchased. The aim is, therefore, to create a classifier that indicates whether a requested transaction is a fraud.

About Credit Card Fraud Detection

In this machine learning project, we solve the problem of detecting credit card fraud transactions using machine numpy, scikit learn, and few other python libraries. We overcome the problem by creating a binary classifier and experimenting with various machine learning techniques to see which fits better.

Credit Card Fraud Dataset

The dataset consists of 31 parameters. Due to confidentiality issues, 28 of the features are the result of the PCA transformation. “Time’ and “Amount” are the only aspects that were not modified with PCA.

There are a total of 284,807 transactions with only 492 of them being fraud. So, the label distribution suffers from imbalance issues.

Please download the dataset for credit card fraud detection project: Anonymized Credit Card Transactions for Fraud Detection

Tools and Libraries used

We use the following libraries and frameworks in credit card fraud detection project.

• Python – 3.x
• Numpy – 1.19.2
• Scikit-learn – 0.24.1
• Matplotlib – 3.3.4
• Imblearn – 0.8.0
• Collections, Itertools

Credit Card Fraud Project Code

Please download the source code of the credit card fraud detection project (which is explained below): Credit Card Fraud Detection Machine Learning Code

Steps to Develop Credit Card Fraud Classifier in Machine Learning

Our approach to building the classifier is discussed in the steps:

1. Perform Exploratory Data Analysis (EDA) on our dataset
2. Apply different Machine Learning algorithms to our dataset
3. Train and Evaluate our models on the dataset and pick the best one.

Step 1. Perform Exploratory Data Analysis (EDA)

There are a total of 284,807 transactions with only 492 of them being fraud. Let’s import the necessary modules, load our dataset, and perform EDA on our dataset. Here is a peek at our dataset:

import pandas as pdfrom collections import Counterimport itertools ## Load the csv file dataframe = pd.read_csv ( “./Desktop/DataFlair/credit_card_fraud_detection/creditcard.csv” ) dataframe.head ()

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