Financial Fraud Alert Review Dataset
Open accessAbstract
The FiFAR dataset, is comprised of 30K bank account opening application instances, accompanied by the judgments of a team of 50 synthetic fraud analysts with realistic decision-making properties on whether or not each instance is a fraudulent application. Each instance contains information regarding the bank account opening application and the applicant, as well as the ground truth label: 0 - legitimate, 1 - fraudulent. Furthermore, each instance contains the prediction of each of the 50 experts, following the same convention as the label. We provide every expert’s prediction for every 30K instances in the Bank Account Fraud dataset (https://www.kaggle.com/datasets/sgpjesus/bank-account-fraud-dataset-neurips-2022/versions/1?select=Base.csv) deemed fraudulent by a fraud detection model, thus simulating an “alert-review” scenario, where experts are tasked with reviewing high-risk bank account opening applications.
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The FiFAR dataset is available for download through the figshare platform.
“The FiFAR dataset31 has been made available for download through the figshare platform (see citation for DOI link).”
From A benchmarking framework and dataset for learning to defer in human-AI decision-making (2025) · cites this paper as [31]
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A benchmarking framework and dataset for learning to defer in human-AI decision-making
Alves, J. V., Leitão, D., Jesus, S., Sampaio, M. O. P., Liébana, J., Saleiro, P., Figueiredo, M. A. T., & Bizarro, P. (2025) · Scientific Data · doi:10.1038/s41597-025-04664-y · Cited by 10 · see citing works
Self-citation
- Authors
- Alves, Leitão, D., Jesus, S., Sampaio, M. O. P., Liébana, J., Saleiro, P., Figueiredo, M. A. T., & Bizarro, P.
- Venue
- figshare
- Publisher
- figshare
- Published
- 2025
- OpenAlex
- W6902395640
- Cited by
- 1
- References
- 0
- Citations by year
- 2025: 1
- Type
- dataset
- Language
- en
- Open access
- green
- figshare.com
- Licence
- Locations
- Figshare (submittedVersion, cc-by)
- Figshare (cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Keywords
- financial fraud detection, expert opinion, fraud classification
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