Here are a few suggestions:

Remember to carefully review and preprocess any dataset you choose, ensuring it meets your project requirements and adheres to data privacy regulations. Additionally, consider the imbalance between fraud and non-fraud cases in the dataset and employ appropriate techniques such as oversampling, undersampling, or using algorithmic methods designed for imbalanced data.

One commonly used dataset for credit card fraud detection is the Credit Card Fraud Detection Dataset available on Kaggle, which contains transactions made by credit cards in September 2013 by European cardholders. This dataset encompasses transactions over a two-day period, including 492 frauds out of 284,807 transactions, making it imbalanced but reflective of real-world scenarios. Additionally, the IEEE-CIS Fraud Detection Dataset on Kaggle offers a more extensive set of real-world features for transactional data, suitable for advanced machine learning models. For cases where real-world data is limited or sensitive, synthetic datasets like the Credit Card Fraud Detection Synthetic Dataset on Kaggle provide an alternative. As with any dataset, it's crucial to understand its limitations, potential biases, and preprocessing requirements while adhering to proper citation and usage protocols.