Financial fraud is a major problem that can cause significant losses for individuals, businesses, and financial institutions. In order to combat this problem, it is important to have tools that can accurately detect fraudulent transactions. One such tool is a machine learning model, which can analyze large amounts of data and identify patterns that may indicate fraudulent activity.
In this blog post, we will develop a machine learning model to detect fraud in financial transactions using Python. We will start by collecting and preprocessing the data, and then we will train and evaluate a model using a number of different techniques.
Data collection and preprocessing:
To create our machine learning model, we will need a dataset of financial transactions that includes both fraudulent and non-fraudulent transactions. There are a number of publicly available datasets that we can use for this purpose, such as the “Credit Card Fraud Detection” dataset from Kaggle.
To begin, we will import the necessary libraries and download the dataset using Pandas.
import pandas as pd
url = "https://www.kaggle.com/mlg-ulb/creditcardfraud/download"
data = pd.read_csv(url)
Next, we will split the data into features and the target variable. The features will be the various attributes of the transactions, such as the amount, the time of the transaction, and the type of card used. The target variable will be a binary label indicating whether the transaction was fraudulent or not.
X = data.drop("Class", axis=1)
y = data["Class"]
It is important to note that fraudulent transactions are often a minority in the dataset, which can lead to imbalanced classes. This can make it more difficult for the model to accurately detect fraud, as it may be biased towards the majority class. To address this issue, we can use techniques such as oversampling or undersampling to balance the classes.
For this example, we will use the RandomUnderSampler from the imblearn library to undersample the majority class.
from imblearn.under_sampling import RandomUnderSampler
sampler = RandomUnderSampler()
X_resampled, y_resampled = sampler.fit_resample(X, y)
Next, we will split the data into training and testing sets. We will use 80% of the data for training and 20% for testing.
from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X_resampled, y_resampled, test_size=0.2, random_state=42)Model development:
Now that our data is prepared, we can start developing our machine learning model. There are a number of different algorithms that we could use for this task, such as decision trees, logistic regression, or support vector machines.
For this example, we will use a random forest classifier, which is an ensemble method that combines the predictions of multiple decision trees to make a final prediction.
To create the random forest classifier, we will use the RandomForestClassifier class from Scikit-learn.
from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier()
Next, we will fit the model to the training data using the fit method.
clf.fit(X_train, y_train)
Model evaluation:
Now that our model is trained, we can evaluate its performance on the test data. We will start by predicting the labels for the test data using the predict method of the RandomForestClassifier object.
y_pred = clf.predict(X_test)
To evaluate the model’s performance, we can use a number of different metrics. One common metric is the accuracy, which measures the proportion of correct predictions made by the model.
We can calculate the accuracy using the accuracy_score function from Scikit-learn.
from sklearn.metrics import accuracy_score
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
Another metric we can use is the confusion matrix, which provides a more detailed breakdown of the model’s performance. The confusion matrix shows the number of true positives, true negatives, false positives, and false negatives made by the model.
We can calculate the confusion matrix using the confusion_matrix function from Scikit-learn.
from sklearn.metrics import confusion_matrix
confusion_matrix = confusion_matrix(y_test, y_pred)
print("Confusion matrix:", confusion_matrix)
It is also a good idea to visualize the confusion matrix using Matplotlib.
import matplotlib.pyplot as plt import seaborn as sns sns.heatmap(confusion_matrix, annot=True)
Here is the complete code for developing a machine learning model to detect fraud in financial transactions using Python:
# Import necessary libraries
import pandas as pd
from imblearn.under_sampling import RandomUnderSampler
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, confusion_matrix
import matplotlib.pyplot as plt
import seaborn as sns
# Download and load dataset
url = "https://www.kaggle.com/mlg-ulb/creditcardfraud/download"
data = pd.read_csv(url)
# Split data into features and target variable
X = data.drop("Class", axis=1)
y = data["Class"]
# Undersample majority class
sampler = RandomUnderSampler()
X_resampled, y_resampled = sampler.fit_resample(X, y)
# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X_resampled, y_resampled, test_size=0.2, random_state=42)
# Create random forest classifier
clf = RandomForestClassifier()
# Fit model to training data
clf.fit(X_train, y_train)
# Predict labels for test data
y_pred = clf.predict(X_test)
# Calculate accuracy
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)