Credit Card Fraud Detection

  

The issue is to spot fraudulent credit card transactions so that credit card firms' consumers aren't charged for products they didn't buy. This has become a huge issue in the modern era because all purchases can be made online with just your credit card information. Credit card fraud detection is critical for any bank or financial business. Even before two-step verification was employed for online purchasing in the United States in the 2010s, many American retail website users were victims of online transaction fraud. When a data breach results in monetary theft and, as a result, the loss of customers' loyalty as well as the company's reputation, it puts organizations, consumers, banks, and merchants in danger. We need to recognise potential fraud so that customers can't be charged for items they didn't buy. This is one of the best and easiest data science project ideas for beginners to work on.

 

In 2017, unauthorized card operations claimed the lives of 16.7 million people. The goal is to develop a classifier that can determine whether a proposed transaction is fraudulent.

 

 

 

The following are the key obstacles in detecting credit card fraud:

 

  • Every day, massive amounts of data are gathered, and the model must be fast enough to respond to the scam in time.
  • Data that is unbalanced, i.e. the vast majority of transactions (99.8%) are not fraudulent, making it extremely difficult to discover the fraudulent ones. Data availability, as the data is generally private.
  • Another big concern is misclassified data, as not every fraudulent transaction is detected and reported.
  • The scammers utilized adaptive approaches against the model.

 

 

Overview:

Fraud can be committed in a variety of ways and in a wide range of industries. We use machine numpy, scikit learn, and a few more python modules to address the challenge of recognising credit card fraud transactions in this data science project. To make a decision, the majority of detection systems combine a number of fraud detection datasets to create a connected picture of both legitimate and invalid payment data. We solved the challenge by developing a binary classifier and experimenting with several data science project approaches to find which one best matches the problem. If you want to learn more about these kinds of projects or more about data science, visit our website, Learnbay: best data science course in Hyderabad which provides different hands-on project like these.

 

 

 

IP address, geolocation, device identity, "BIN" data, global latitude/longitude, history transaction trends, and actual transaction information must all be considered while making this decision. There are 31 parameters in the dataset. In practice, this means merchants and issuers use analytically based answers to detect fraud by using a set of business rules or analytical algorithms to internal and external data. The PCA transformation resulted in the loss of 28 features due to confidentiality concerns. The only aspects of PCA that were not changed were "Time" and "Amount."

 

Credit Card Fraud Detection with data science is a method that involves a Data Science team investigating data and developing a model that will uncover and prevent fraudulent transactions. Fraudsters are always inventing new fraud patterns, particularly to adapt to fraud detection systems. This is accomplished by combining all relevant aspects of cardholder transactions, such as the date, user zone, product category, amount, provider, client's behavioral patterns, and so on. Data science models that are never updated are insufficient because they do not account for changes and trends in client spending patterns, such as throughout holiday seasons and across geographic regions. The data is then fed into a model that has been gradually taught to look for patterns and rules in order to determine if a transaction is fraudulent or not. Fraud monitoring and detection systems are used by all major banks, including Chase.

 

 

 

 

 

Importing all the necessary Libraries

 

 

# import the necessary packages

 

import numpy as np

 

import pandas as pd

 

import matplotlib.pyplot as plt

 

import seaborn as sns

 

from matplotlib import gridspec

 

 

 

Loading the Data

 

 

# copy the path for the csv file

 

data = pd.read_csv("credit.csv")

 

Code : Understanding the Data

 

 

 

# Grab a peek at the data

 

data.head()

 

 

 

Describing the Data

 

 

# Print the shape of the data

 

print(data.shape)

 

print(data.describe())

 

 

 

Imbalance in the data

 

 

fraud = data[data['Class'] == 1]

 

valid = data[data['Class'] == 0]

 

outlierFraction = len(fraud)/float(len(valid))

 

print(outlierFraction)

 

print('Fraud Cases: {}'.format(len(data[data['Class'] == 1])))

 

print('Valid Transactions: {}'.format(len(data[data['Class'] == 0])))

 

 

 

 

 

For Fraudulent Transaction, print the amount data.

 

 

print(“Amount details of the fraudulent transaction”)

 

fraud.Amount.describe()

 

 

 

For a Normal Transaction, print the amount details.

 

 

print(“details of valid transaction”)

 

valid.Amount.describe()

 

 

 

 

 

Plotting the Correlation Matrix

 

 

# Correlation matrix

 

corrmat = data.corr()

 

fig = plt.figure(figsize = (12, 9))

 

sns.heatmap(corrmat, vmax = .8, square = True)

 

plt.show()

 

 

 

Separating the X and the Y values

 

Dividing the data into inputs parameters and outputs value format

 

 

 

X = data.drop(['Class'], axis = 1)

 

Y = data["Class"]

 

print(X.shape)

 

print(Y.shape)

 

xData = X.values

 

yData = Y.values

 

 

 

 

 

Skicit Learn is used to create a Random Forest Model.

 

 

from sklearn.ensemble import RandomForestClassifier

 

# random forest model creation

 

rfc = RandomForestClassifier()

 

rfc.fit(xTrain, yTrain)

 

# predictions

 

yPred = rfc.predict(xTest)

 

 

 

Creating a variety of evaluative parameters

 

 

# Evaluating the classifier

 

# printing every score of the classifier

 

# scoring in anything

 

from sklearn.metrics import classification_report, accuracy_score

 

from sklearn.metrics import precision_score, recall_score

 

from sklearn.metrics import f1_score, matthews_corrcoef

 

from sklearn.metrics import confusion_matrix

 

 

 

n_outliers = len(fraud)

 

n_errors = (yPred != yTest).sum()

 

print("The model used is Random Forest classifier")

 

 

 

acc = accuracy_score(yTest, yPred)

 

print("The accuracy is {}".format(acc))

 

 

 

prec = precision_score(yTest, yPred)

 

print("The precision is {}".format(prec))

 

 

 

rec = recall_score(yTest, yPred)

 

print("The recall is {}".format(rec))

 

 

 

f1 = f1_score(yTest, yPred)

 

print("The F1-Score is {}".format(f1))

 

 

 

MCC = matthews_corrcoef(yTest, yPred)

 

print("The Matthews correlation coefficient is{}".format(MCC))

 

 

 

# printing the confusion matrix

 

LABELS = ['Normal', 'Fraud']

 

conf_matrix = confusion_matrix(yTest, yPred)

 

plt.figure(figsize =(12, 12))

 

sns.heatmap(conf_matrix, xticklabels = LABELS,

 

            yticklabels = LABELS, annot = True, fmt ="d");

 

plt.title("Confusion matrix")

 

plt.ylabel('True class')

 

plt.xlabel('Predicted class')

 

plt.show()

 

 

 

Final lines

Fraud is a serious issue for the entire credit card business, and it is becoming more prevalent as electronic money transfers become more common. We constructed a binary classifier using the Random Forest technique to detect credit card fraud transactions in our python data science project. Credit card issuers should consider implementing advanced Credit Card Fraud Prevention and Fraud Detection methods to effectively prevent criminal actions such as the leakage of bank account information, skimming, counterfeit credit cards, the theft of billions of dollars annually, and the loss of reputation and customer loyalty.

 

We learned and utilized strategies to handle class imbalance issues through this project, and we obtained a 99 percent accuracy rate. Based on information about each cardholder's behavior, data science-based methods can continuously enhance the accuracy of fraud protection. Because some fraudsters conduct frauds once using online channels and then transition to other ways, fraud detection systems must detect online transactions using unsupervised learning. So, hurry and start learning from the Learnbay data science course in Hyderabad as well as start your exciting project.

 

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