In today's digital era, where financial transactions are predominantly conducted online, the risk of credit card fraud has escalated significantly. According to a report by the Federal Trade Commission (FTC), credit card fraud was the most common type of identity theft reported in 2020, accounting for 39% of all identity theft reports. To combat this growing threat, financial institutions and payment processors are increasingly turning to advanced technologies such as machine learning (ML) to detect and prevent fraudulent activities. In this blog post, we delve into the intricate world of machine learning in credit card fraud detection, exploring its methodologies, challenges, and effectiveness in safeguarding financial transactions.
Understanding Credit Card Fraud
Credit card fraud refers to unauthorized or fraudulent transactions made using someone else's credit or debit card information. Fraudulent activities can take various forms, including stolen card numbers, counterfeit cards, card-not-present (CNP) transactions, identity theft, and account takeover. These fraudulent transactions not only result in financial losses for both consumers and businesses but also erode trust in the financial system.
Traditional Methods vs. Machine Learning
Traditionally, financial institutions relied on rule-based systems and manual review processes to detect fraudulent transactions. While these methods were somewhat effective, they often struggled to keep pace with the evolving tactics of fraudsters. Machine learning, on the other hand, offers a more dynamic and data-driven approach to fraud detection. By analyzing vast amounts of transactional data in real-time, ML algorithms can identify patterns, anomalies, and indicators of fraudulent behavior with greater accuracy and efficiency.
Methodologies in Machine Learning Fraud Detection:
- Supervised Learning: Supervised learning algorithms are trained on labeled datasets containing examples of both legitimate and fraudulent transactions. These algorithms learn to distinguish between the two classes and make predictions on new, unseen data based on the patterns they have learned during training. Common supervised learning algorithms used in credit card fraud detection include logistic regression, decision trees, random forests, and support vector machines (SVM).
- Unsupervised Learning: Unsupervised learning algorithms do not require labeled data for training. Instead, they learn the underlying structure of the data and identify anomalies or outliers that deviate significantly from the norm. Clustering algorithms such as K-means clustering and density-based clustering, as well as anomaly detection algorithms like isolation forest and autoencoders, are commonly used in unsupervised fraud detection.
- Semi-Supervised Learning: Semi-supervised learning combines elements of both supervised and unsupervised learning. It leverages a small amount of labeled data along with a larger pool of unlabeled data to improve the performance of fraud detection models. This approach is particularly useful when labeled data is scarce or expensive to obtain.
Challenges and Considerations:
- Imbalanced Datasets:Fraudulent transactions are often rare compared to legitimate transactions, leading to imbalanced datasets that can bias the model towards the majority class.
- Concept Drift:Fraud patterns evolve over time, necessitating continuous monitoring and adaptation of ML models to detect new and emerging threats.
- Interpretability:Black-box nature of some ML algorithms makes it difficult to interpret their decisions, which can pose challenges in explaining and justifying fraud detection outcomes to stakeholders.
- Adversarial Attacks:Fraudsters may attempt to evade detection by deliberately manipulating transactions to exploit vulnerabilities in ML models, highlighting the importance of robustness and security in fraud detection systems.
Download the data set from the following link Dataset Link: https://www.kaggle.com/mlg-ulb/creditcardfraud and run the following code in google colab to train your model.
The below code is .Python file.
# %% [markdown]
# ## Dataset Link: https://www.kaggle.com/mlg-ulb/creditcardfraud
# %%
## required libraries
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
pd.set_option('display.max_columns', None)
sns.set_style('darkgrid')
# %%
## reading dataset
df = pd.read_csv('creditcard.csv')
## displaying first five rows
df.head()
# %%
## shape of dataset
df.shape
# %%
## checking null values
df.isnull().sum()
# %%
## count the occurance of unique values in class column
df.Class.value_counts()
# %%
## countplot of classes
plt.figure(figsize=(10, 5))
sns.countplot(df['Class'], log=True);
# %%
## checking correlation
plt.figure(figsize=(25,25))
plt.title("Correlation Matrix")
sns.heatmap(round(df.corr(), 2), annot=True);
# %%
## checking correlation of 'dependent' variable with each "independent" variable
df.corr()[['Class']].sort_values(by='Class')[:-1]
# %%
## dependent and independent variables
X = df.iloc[:, :-1]
y = df.iloc[:, -1]
# %%
X.head()
# %%
y.head()
# %%
## train test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=0)
# %%
## Standard Scaler
sc= StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
# %%
print(X_train.shape)
print(X_test.shape)
# %% [markdown]
# ## Logistic Regression
# %%
lg = LogisticRegression()
## fit on training data
lg.fit(X_train, y_train)
# %%
## prediction
pred = lg.predict(X_test)
print('Classification Report: \n', classification_report(y_test, pred))
print("-" * 100)
print()
print('Accuracy Score: ', accuracy_score(y_test, pred))
print("-" * 100)
print()
plt.figure(figsize=(10, 10))
sns.heatmap(confusion_matrix(y_test, pred), annot=True, fmt='g');
# %% [markdown]
# ## Random Forest
# %%
rf = RandomForestClassifier()
## fit on training data
rf.fit(X_train, y_train)
# %%
## prediction
pred = rf.predict(X_test)
print('Classification Report: \n', classification_report(y_test, pred))
print("-" * 100)
print()
print('Accuracy Score: ', accuracy_score(y_test, pred))
print("-" * 100)
print()
plt.figure(figsize=(10, 10))
sns.heatmap(confusion_matrix(y_test, pred), annot=True, fmt='g');
# %%Conclusion
In conclusion, machine learning represents a powerful tool in the fight against credit card fraud, enabling financial institutions to stay one step ahead of fraudsters and protect the integrity of the financial system. By leveraging sophisticated algorithms and big data analytics, ML-based fraud detection systems can identify suspicious patterns and anomalies in real-time, mitigating financial losses and safeguarding consumer trust. However, it is essential to recognize the ongoing arms race between fraudsters and fraud detection systems, emphasizing the need for continuous innovation and collaboration across industry stakeholders to stay ahead of emerging threats and ensure financial security for all.
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