Imagine a world where fraudulent credit card transactions can be predicted and prevented with the power of cutting-edge machine learning models. In this groundbreaking project, we set out to do just that.
Our mission is to analyze customer-level data collected during a research collaboration between Worldline and the Machine Learning Group. This dataset, available on the Kaggle Website, consists of a total of 284,807 transactions, with a mere 492 of them being fraudulent. Given this highly imbalanced dataset, our first challenge is to address this imbalance before building our predictive models.
For banks, retaining high-value customers is the ultimate goal. However, banking fraud poses a significant threat to this objective. Fraudulent activities not only result in substantial financial losses but also erode trust and credibility, affecting both banks and their customers.
According to a Nilson report, global banking fraud losses were projected to reach $30 billion by 2020. With the increasing use of digital payment channels, the number of fraudulent transactions is on the rise, using new and diverse techniques.
In the banking industry, the application of machine learning for credit card fraud detection is not just a trend; it's a necessity. Machine learning empowers financial institutions to implement proactive monitoring and fraud prevention mechanisms, reducing manual reviews, costly chargebacks, and denials of legitimate transactions.
Credit card fraud encompasses various dishonest actions aimed at gaining unauthorized access to financial resources. One of the most common forms of fraud is "skimming," which involves duplicating information from the magnetic strip of a card. Other fraudulent activities include:
- Manipulating or altering genuine cards
- Creating counterfeit cards
- Using stolen or lost credit cards
- Engaging in fraudulent telemarketing schemes
To embark on our journey, let's first access the dataset using this link. This dataset includes credit card transactions made by European cardholders over a two-day period in September 2013. Out of a total of 284,807 transactions, a mere 492 were fraudulent, making the dataset highly imbalanced.
The dataset has been subjected to Principal Component Analysis (PCA) to maintain confidentiality. The primary components obtained through PCA are represented as 'V1' to 'V28'. The 'time' feature represents the seconds elapsed between the first transaction and subsequent ones, while 'amount' denotes the transaction value. The 'class' feature categorizes transactions as 1 (fraudulent) or 0 (legitimate).
Our project journey unfolds in four pivotal stages:
- Data Exploration: Dive into the dataset, grasp its features, and select those crucial for the final model.
- Exploratory Data Analysis (EDA): Conduct univariate and bivariate analyses, and perform any necessary feature transformations. Given that Gaussian variables are used, scaling may not be required. Still, keep an eye out for skewness, which could impact model performance.
- Train/Test Split: Split the data into training and testing sets. To validate the models, use k-fold cross-validation, ensuring that the minority class is well-represented in the test folds.
- Model Building and Hyperparameter Tuning: This is where the magic happens. Experiment with various models and fine-tune their hyperparameters until you achieve the desired performance. Explore different sampling techniques to enhance your model's accuracy.
- Model Evaluation: It's essential to evaluate the models using metrics that reflect the business goal, especially considering the imbalanced data. Identifying fraudulent transactions accurately is the top priority.
Join us on this journey to revolutionize credit card fraud detection and safeguard the financial well-being of individuals and institutions alike.