The Bank Account Fraud (BAF) suite of datasets has been published at NeurIPS 2022 and it comprises a total of 6 different synthetic bank account fraud tabular datasets. BAF is a realistic, complete, and robust test bed to evaluate novel and existing methods in ML and fair ML, and the first of its kind!

This suite of datasets is: - Realistic, based on a present-day real-world dataset for fraud detection; - Biased, each dataset has distinct controlled types of bias; - Imbalanced, this setting presents a extremely low prevalence of positive class; - Dynamic, with temporal data and observed distribution shifts;
- Privacy preserving, to protect the identity of potential applicants we have applied differential privacy techniques (noise addition), feature encoding and trained a generative model (CTGAN).

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Each dataset is composed of: - 1 million instances; - 30 realistic features used in the fraud detection use-case; - A column of “month”, providing temporal information about the dataset; - Protected attributes, (age group, employment status and % income).

Detailed information (datasheet) on the suite: https://github.com/feedzai/bank-account-fraud/blob/main/documents/datasheet.pdf

Check out the github repository for more resources and some example notebooks: https://github.com/feedzai/bank-account-fraud

Read the NeurIPS 2022 paper here: https://arxiv.org/abs/2211.13358

Learn more about Feedzai Research here: https://research.feedzai.com/

Please, use the following citation of BAF dataset suite @article{jesusTurningTablesBiased2022, title={Turning the {{Tables}}: {{Biased}}, {{Imbalanced}}, {{Dynamic Tabular Datasets}} for {{ML Evaluation}}}, author={Jesus, S{\'e}rgio and Pombal, Jos{\'e} and Alves, Duarte and Cruz, Andr{\'e} and Saleiro, Pedro and Ribeiro, Rita P. and Gama, Jo{\~a}o and Bizarro, Pedro}, journal={Advances in Neural Information Processing Systems}, year={2022} }

Vehicle Insurance Fraud Detection

Vehicle insurance fraud involves conspiring to make false or exaggerated claims involving property damage or personal injuries following an accident. Some common examples include staged accidents where fraudsters deliberately “arrange” for accidents to occur; the use of phantom passengers where people who were not even at the scene of the accident claim to have suffered grievous injury, and make false personal injury claims where personal injuries are grossly exaggerated.

About this dataset

This dataset contains vehicle dataset - attribute, model, accident details, etc along with policy details - policy type, tenure etc. The target is to detect if a claim application is fraudulent or not - FraudFound_P

Below is a draft DMP–style description of your credit‐card fraud detection experiment, modeled on the antiquities example:

1. Dataset Description

Research Domain
This work resides in the domain of financial fraud detection and applied machine learning. We focus on detecting anomalous credit‐card transactions in real time to reduce financial losses and improve trust in digital payment systems.

Purpose
The goal is to train and evaluate a binary classification model that flags potentially fraudulent transactions. By publishing both the code and data splits via FAIR repositories, we enable reproducible benchmarking of fraud‐detection algorithms and support future research on anomaly detection in transaction data.

Data Sources
We used the publicly available credit‐card transaction dataset from Kaggle (original source: https://www.kaggle.com/mlg-ulb/creditcardfraud), which contains anonymized transactions made by European cardholders over two days in September 2013. The dataset includes 284 807 transactions, of which 492 are fraudulent.

Method of Dataset Preparation

1. Schema validation: Renamed columns to snake_case (e.g. transaction_amount, is_declined) so they conform to DBRepo’s requirements.

2. Data import: Uploaded the full CSV into DBRepo, assigned persistent identifiers (PIDs).

3. Splitting: Programmatically derived three subsets—training (70%), validation (15%), test (15%)—using range‐based filters on the primary key actionnr. Each subset was materialized in DBRepo and assigned its own PID for precise citation.

4. Cleaning: Converted the categorical flags (is_declined, isforeigntransaction, ishighriskcountry, isfradulent) from “Y”/“N” to 1/0 and dropped non‐feature identifiers (actionnr, merchant_id).

5. Modeling: Trained a RandomForest classifier on the training split, tuned on validation, and evaluated on the held‐out test set.

2. Technical Details

Dataset Structure

• The raw data is a single CSV with columns:

  • actionnr (integer transaction ID)

  • merchant_id (string)

  • average_amount_transaction_day (float)

  • transaction_amount (float)

  • is_declined, isforeigntransaction, ishighriskcountry, isfradulent (binary flags)

  • total_number_of_declines_day, daily_chargeback_avg_amt, sixmonth_avg_chbk_amt, sixmonth_chbk_freq (numeric features)

Naming Conventions

• All columns use lowercase snake_case.

• Subsets are named creditcard_training, creditcard_validation, creditcard_test in DBRepo.

• Files in the code repo follow a clear structure:

  ├── data/         # local copies only; raw data lives in DBRepo 
  ├── notebooks/Task.ipynb 
  ├── models/rf_model_v1.joblib 
  ├── outputs/        # confusion_matrix.png, roc_curve.png, predictions.csv 
  ├── README.md 
  ├── requirements.txt 
  └── codemeta.json 
  

Required Software

• Python 3.9+

• pandas, numpy (data handling)

• scikit-learn (modeling, metrics)

• matplotlib (visualizations)

• dbrepo‐client.py (DBRepo API)

• requests (TU WRD API)

Additional Resources

• Original dataset: https://www.kaggle.com/mlg-ulb/creditcardfraud

• Scikit-learn docs: https://scikit-learn.org/stable

• DBRepo API guide: via the starter notebook’s dbrepo_client.py template

• TU WRD REST API spec: https://test.researchdata.tuwien.ac.at/api/docs

3. Further Details

Data Limitations

• Highly imbalanced: only ~0.17% of transactions are fraudulent.

• Anonymized PCA features (V1–V28) hidden; we extended with domain features but cannot reverse engineer raw variables.

• Time‐bounded: only covers two days of transactions, may not capture seasonal patterns.

Licensing and Attribution

• Raw data: CC-0 (per Kaggle terms)

• Code & notebooks: MIT License

• Model artifacts & outputs: CC-BY 4.0

• DUWRD records include ORCID identifiers for the author.

Recommended Uses

• Benchmarking new fraud‐detection algorithms on a standard imbalanced dataset.

• Educational purposes: demonstrating model‐training pipelines, FAIR data practices.

• Extension: adding time‐series or deep‐learning models.

Known Issues

• Possible temporal leakage if date/time features not handled correctly.

• Model performance may degrade on live data due to concept drift.

• Binary flags may oversimplify nuanced transaction outcomes.

Dataset

This dataset was created by Muste A(M.A)

Contents

Synthetic transactional data with labels for fraud detection. For more information, see: https://www.kaggle.com/ntnu-testimon/paysim1/version/2

The dataset contains transactions made by credit cards in September 2013 by European cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions. The dataset is 0.15 GB large.

The dataset has been collected and analysed during a research collaboration of Worldline and the Machine Learning Group (http://mlg.ulb.ac.be) of ULB (Université Libre de Bruxelles) on big data mining and fraud detection.

Dataset

This dataset was created by IAbhishekBhardwaj

Contents

The dataset has been released by [1], which had been collected and analysed during a research collaboration of Worldline and the Machine Learning Group (http://mlg.ulb.ac.be) of Université Libre de Bruxelles (ULB) on big data mining and fraud detection. [1] Pozzolo, A. D., Caelan, O., Johnson, R. A., and Bontempi, G. (2015). Calibrating Probability with Undersampling for Unbalanced Classification. 2015 IEEE Symposium Series on Computational, pp. 159-166, doi: 10.1109/SSCI.2015.33 open source kaggle : https://www.kaggle.com/mlg-ulb/creditcardfraud

Credit Card Fraud Detection Dataset (Updated)

This dataset contains 284,807 transactions from a credit card company, where 492 transactions are fraudulent. The data is highly imbalanced, with only a small fraction of transactions being fraudulent. The dataset is commonly used to build and evaluate fraud detection models.

Dataset Details:

• Number of Transactions: 284,807
• Fraudulent Transactions: 492 (Highly Imbalanced)
• Features:
  • 28 anonymized features (V1 to V28)
  • Transaction amount
  • Timestamp
• Label:
  • 0: Legitimate
  • 1: Fraudulent

Data Preprocessing:

• SMOTE (Synthetic Minority Oversampling Technique) has been applied to address the class imbalance in the dataset, generating synthetic examples for the minority class (fraudulent transactions).
• Additional Operations: Various preprocessing steps were performed, including data cleaning and feature engineering, to ensure the quality of the dataset for model training.

Processed Files:

The dataset has been split into training and testing sets and saved in the following files: - X_train.csv: Feature data for the training set - X_test.csv: Feature data for the testing set - y_train.csv: Labels for the training set (fraudulent or legitimate) - y_test.csv: Labels for the testing set

This updated dataset is ready to be used for training and evaluating machine learning models, specifically designed for credit card fraud detection tasks.

This description highlights the key aspects of the dataset, including its preprocessing steps and the availability of the processed files for ease of use.

Dataset

This dataset was created by AYUSH VARSHNEY

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Dataset

This dataset was created by Arshiya Kishore

Released under MIT

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Credit Card Fraud Detection Dataset

Uncover fraudulent transactions with this anonymized, PCA-transformed dataset. Perfect for building and testing fraud detection algorithms!

Dataset Overview

• Objective: Detect fraudulent credit card transactions using anonymized features- - - -

• Samples: 1,000 transactions

• Features: 7 columns (5 PCA components + Transaction Amount + Target)

Class Distribution:

• Legit (Class 0): 993 transactions (~99.3%)

• Fraud (Class 1): 7 transactions (~0.7%)

• Key Challenge: Extreme class imbalance – realistic representation of fraud patterns

Features Description

Feature Description Characteristics

V1-V5 Anonymized principal components PCA-transformed numerical features; preserves >transaction patterns while hiding sensitive details Amount Transaction value Highly variable (min: $0.20, max: $1,916.06); critical for fraud analysis Class Target variable Binary labels: • 0 = Legitimate transaction • 1 = Fraudulent transaction Key Insights & Patterns

Fraud Indicators:

• Fraudulent transactions occur across diverse amounts (low: $1.83 → high: $1,916)

• No obvious amount threshold for fraud – requires nuanced modeling

Sample fraud cases:

1. V1:0.579, V2:-0.384, Amount:1916.06

2. V1:1.023, V2:-0.638, Amount:1094.42

Data Characteristics:

1. V1-V5 Distributions:

2. V1: Concentrated near zero (mean ≈ -0.1)

3. V2: Wider spread (mean ≈ 0.05)

4. V3-V5: Asymmetric distributions

Amount Distribution:

1. Right-skewed – most transactions < $500

2.Fraud cases span low and high values

Class Imbalance:

 - Severe skew: 993:7 legit-to-fraud ratio

 - Models must optimize for recall/precision over accuracy

Analysis Challenges

⚠️ Class Imbalance: Standard accuracy metrics misleading

🔍 Feature Interpretation: PCA components lack real-world context

📊 Non-linear Patterns: Complex interactions between V1-V5

⚡ High Stakes: False negatives (missed fraud) costlier than false positives

Recommended Applications Fraud Detection Models:

Logistic Regression (with class weighting)

Random Forests / XGBoost (handle non-linearities)

Isolation Forests (anomaly detection)

Evaluation Focus:

Precision-Recall Curves > ROC-AUC

F2-Score (prioritize recall)

Confusion matrix analysis

Advanced Techniques:

SMOTE/ADASYN for oversampling

Autoencoders for anomaly detection

Feature engineering: Amount-to-Var ratios

Dataset Source & Ethics Origin: Synthetic dataset mirroring real-world financial patterns

Anonymization: Original features transformed via PCA for privacy compliance

Bias Consideration: Geographic/cultural biases possible in source data

Potential Use Cases

🏦 Banking: Real-time transaction monitoring systems

📱 FinTech Apps: Fraud detection APIs for payment gateways

🎓 Education: Imbalanced classification tutorials

🏆 Kaggle Competitions: Lightweight fraud detection challenge

Example Project Idea "Minimalist Fraud Detector":

# python
from imblearn.pipeline import make_pipeline
from sklearn.ensemble import RandomForestClassifier

model = make_pipeline(
  RobustScaler(), 
  SMOTE(sampling_strategy=0.3), 
  RandomForestClassifier(class_weight={0:1, 1:15}) 
)
Optimize for: Recall @ Precision > 0.85

Dataset Summary markdown | Feature | Mean | Std | Min | Max | |----------|----------|----------|-----------|-----------| | V1 | -0.11 | 1.02 | -3.24 | 3.85 | | V2 | 0.05 | 1.01 | -2.94 | 2.60 | | V3 | 0.02 | 0.98 | -3.02 | 2.95 |
| Amount | 250.32 | 190.19 | 0.20 | 1916.06 |

Phishing Email Dataset

This dataset on Hugging Face is a direct copy of the 'Phishing Email Detection' dataset from Kaggle, shared under the GNU Lesser General Public License 3.0. The dataset was originally created by the user 'Cyber Cop' on Kaggle. For complete details, including licensing and usage information, please visit the original Kaggle page.

Dataset

This dataset was created by TienNguyen143

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Dataset

This dataset was created by kroder

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Dataset

This dataset was created by Sriseshagiri

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Inspiration

If you find this dataset useful, pls drop a like.

Description

Banks and Payment systems are often exposed to fraudulent transactions and constantly improve systems to track them.

The synthetic dataset below contains 6.3mln transactions with 10 features.

Context

Simple fraud detection dataset. The target is EVENT_LABEL 1 = fraud 0 = not fraud

Content

This is a great dataset to practice classification tasks with and challenge students with.

Inspiration

1. relatively rare event detection
2. what variables are important
3. dealing with high frequency categorical data like user agent, card bin, and postal codes.

Dataset

This dataset was created by Gungun Shukla15

Released under CC0: Public Domain

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Dataset

This dataset was created by Emily Smith

Released under Data files © Original Authors

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