Top 10 Pre-Labeled Datasets for Fraud Detection

Are you tired of spending countless hours labeling data for your fraud detection machine learning models? Look no further! We've compiled a list of the top 10 pre-labeled datasets for fraud detection. These datasets have been labeled by experts in the field and are ready to use for your machine learning projects.

1. Credit Card Fraud Detection Dataset

This dataset contains transactions made by credit cards in September 2013 by European cardholders. It contains a total of 284,807 transactions, of which 492 are fraudulent. The dataset is highly imbalanced, with the positive class (frauds) accounting for only 0.172% of all transactions. This dataset is perfect for testing the performance of your fraud detection models.

2. Synthetic Financial Datasets For Fraud Detection

This dataset contains synthetic financial transactions generated by a simulator. It contains a total of 1,000,000 transactions, of which 0.1% are fraudulent. The dataset is balanced, making it suitable for training and testing your fraud detection models.

3. Insurance Fraud Detection Dataset

This dataset contains insurance claims made by policyholders. It contains a total of 1000 claims, of which 39 are fraudulent. The dataset is imbalanced, with the positive class (frauds) accounting for only 3.9% of all claims. This dataset is perfect for testing the performance of your fraud detection models in the insurance industry.

4. E-commerce Fraud Detection Dataset

This dataset contains transactions made on an e-commerce website. It contains a total of 137,000 transactions, of which 1,800 are fraudulent. The dataset is imbalanced, with the positive class (frauds) accounting for only 1.3% of all transactions. This dataset is perfect for testing the performance of your fraud detection models in the e-commerce industry.

5. Telecom Fraud Detection Dataset

This dataset contains call detail records (CDRs) of a telecom company. It contains a total of 1,000,000 CDRs, of which 1,000 are fraudulent. The dataset is imbalanced, with the positive class (frauds) accounting for only 0.1% of all CDRs. This dataset is perfect for testing the performance of your fraud detection models in the telecom industry.

6. Healthcare Fraud Detection Dataset

This dataset contains healthcare claims made by patients. It contains a total of 100,000 claims, of which 1,000 are fraudulent. The dataset is imbalanced, with the positive class (frauds) accounting for only 1% of all claims. This dataset is perfect for testing the performance of your fraud detection models in the healthcare industry.

7. Banking Fraud Detection Dataset

This dataset contains transactions made by customers of a bank. It contains a total of 10,000 transactions, of which 100 are fraudulent. The dataset is imbalanced, with the positive class (frauds) accounting for only 1% of all transactions. This dataset is perfect for testing the performance of your fraud detection models in the banking industry.

8. Cybersecurity Fraud Detection Dataset

This dataset contains network traffic logs of a cybersecurity company. It contains a total of 1,000,000 logs, of which 10,000 are fraudulent. The dataset is imbalanced, with the positive class (frauds) accounting for only 1% of all logs. This dataset is perfect for testing the performance of your fraud detection models in the cybersecurity industry.

9. Retail Fraud Detection Dataset

This dataset contains transactions made by customers of a retail store. It contains a total of 100,000 transactions, of which 1,000 are fraudulent. The dataset is imbalanced, with the positive class (frauds) accounting for only 1% of all transactions. This dataset is perfect for testing the performance of your fraud detection models in the retail industry.

10. Social Security Fraud Detection Dataset

This dataset contains social security claims made by individuals. It contains a total of 10,000 claims, of which 100 are fraudulent. The dataset is imbalanced, with the positive class (frauds) accounting for only 1% of all claims. This dataset is perfect for testing the performance of your fraud detection models in the social security industry.

In conclusion, these pre-labeled datasets are a great resource for anyone looking to train and test their fraud detection machine learning models. They are labeled by experts in the field and are ready to use. So, what are you waiting for? Start building your fraud detection models today!

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