The introduction of online payment systems has helped a lot in the ease of payments. But, at the same time, it increased in payment frauds. Online payment frauds can happen with anyone using any payment system, especially while making payments using a credit card. That is why detecting online payment fraud is very important for credit card companies to ensure that the customers are not getting charged for the products and services they never paid. If you want to learn how to detect online payment frauds, this article is for you. In this article, I will take you through the task of online payments fraud detection with machine learning using Python.
To identify online payment fraud with machine learning, we need to train a machine learning model for classifying fraudulent and non-fraudulent payments. For this, we need a dataset containing information about online payment fraud, so that we can understand what type of transactions lead to fraud. For this task, I collected a dataset from Kaggle, which contains historical information about fraudulent transactions which can be used to detect fraud in online payments. Below are all the columns from the dataset I’m using here:
I hope you now know about the data I am using for the online payment fraud detection task. Now in the section below, I’ll explain how we can use machine learning to detect online payment fraud using Python.
I will start this task by importing the necessary Python libraries and the dataset we need for this task: 1
import pandas as pd
import numpy as np
data = pd.read_csv("credit card.csv")print(data.head())
step type amount nameOrig oldbalanceOrg newbalanceOrig \ 0 1 PAYMENT 9839.64 C1231006815 170136.0 160296.36 1 1 PAYMENT 1864.28 C1666544295 21249.0 19384.72 2 1 TRANSFER 181.00 C1305486145 181.0 0.00 3 1 CASH_OUT 181.00 C840083671 181.0 0.00 4 1 PAYMENT 11668.14 C2048537720 41554.0 29885.86 nameDest oldbalanceDest newbalanceDest isFraud isFlaggedFraud 0 M1979787155 0.0 0.0 0 0 1 M2044282225 0.0 0.0 0 0 2 C553264065 0.0 0.0 1 0 3 C38997010 21182.0 0.0 1 0 4 M1230701703 0.0 0.0 0 0
Now, let’s have a look at whether this dataset has any null values or not: 1
print(data.isnull().sum())
step 0 type 0 amount 0 nameOrig 0 oldbalanceOrg 0 newbalanceOrig 0 nameDest 0 oldbalanceDest 0 newbalanceDest 0 isFraud 0 isFlaggedFraud 0 dtype: int64
So this dataset does not have any null values. Before moving forward, now, let’s have a look at the type of transaction mentioned in the dataset: 1
# Exploring transaction type
print(data.type.value_counts())
CASH_OUT 2237500 PAYMENT 2151495 CASH_IN 1399284 TRANSFER 532909 DEBIT 41432 Name: type, dtype: int64
type = data["type"].value_counts()
transactions = type.index
quantity = type.values
import plotly.express as px
figure = px.pie(data, values=quantity, names=transactions,hole = 0.5, title="Distribution of Transaction Type")
figure.show()
Now let’s have a look at the correlation between the features of the data with the isFraud column: 1
# Checking correlation
correlation = data.corr()
print(correlation["isFraud"].sort_values(ascending=False))
isFraud 1.000000 amount 0.076688 isFlaggedFraud 0.044109 step 0.031578 oldbalanceOrg 0.010154 newbalanceDest 0.000535 oldbalanceDest -0.005885 newbalanceOrig -0.008148 Name: isFraud, dtype: float64
Now let’s transform the categorical features into numerical. Here I will also transform the values of the isFraud column into No Fraud and Fraud labels to have a better understanding of the output: 1
data["type"] = data["type"].map({"CASH_OUT": 1, "PAYMENT": 2, "CASH_IN": 3, "TRANSFER": 4, "DEBIT": 5})data["isFraud"] = data["isFraud"].map({0: "No Fraud", 1: "Fraud"})print(data.head())
step type amount nameOrig oldbalanceOrg newbalanceOrig \ 0 1 2 9839.64 C1231006815 170136.0 160296.36 1 1 2 1864.28 C1666544295 21249.0 19384.72 2 1 4 181.00 C1305486145 181.0 0.00 3 1 1 181.00 C840083671 181.0 0.00 4 1 2 11668.14 C2048537720 41554.0 29885.86 nameDest oldbalanceDest newbalanceDest isFraud isFlaggedFraud 0 M1979787155 0.0 0.0 No Fraud 0 1 M2044282225 0.0 0.0 No Fraud 0 2 C553264065 0.0 0.0 Fraud 0 3 C38997010 21182.0 0.0 Fraud 0 4 M1230701703 0.0 0.0 No Fraud 0
Now let’s train a classification model to classify fraud and non-fraud transactions. Before training the model, I will split the data into training and test sets: 1
# splitting the data
from sklearn.model_selection import train_test_split
x = np.array(data[["type", "amount", "oldbalanceOrg", "newbalanceOrig"]])
y = np.array(data[["isFraud"]])
Now let’s train the online payments fraud detection model: 1
# training a machine learning model
from sklearn.tree import DecisionTreeClassifier
3
xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size=0.10, random_state=42)
model = DecisionTreeClassifier()
model.fit(xtrain, ytrain)
print(model.score(xtest, ytest))
0.9997391011878755
Now let’s classify whether a transaction is a fraud or not by feeding about a transaction into the model: 1
# predictio
#features = [type, amount, oldbalanceOrg, newbalanceOrig]
features = np.array([[4, 9000.60, 9000.60, 0.0]])
print(model.predict(features))
['Fraud']
So this is how we can detect online payments fraud with machine learning using Python. Detecting online payment frauds is one of the applications of data science in finance. I hope you liked this article on online payments fraud detection with machine learning using Python. Feel free to ask valuable questions in the comments section below.
