Where can researchers access legal, open BIN datasets for payment system analysis?
Executive summary
Researchers seeking legal, open BIN/IIN data can start with community-maintained BIN lists such as the GitHub bin-list-data repository, which publishes a CSV of BIN records under a permissive CC-BY license [1], and supplement those with broader payments collections on platforms like data.world [2] and curated dataset directories [3]; for transaction-pattern work where live BIN-linked transaction records are unavailable or restricted, well-known synthetic generators and simulated datasets such as PaySim/BankSim provide legally shareable alternatives [4] [5] [6] [7]. Commercial data marketplaces and specialist vendors aggregate richer, proprietary payment feeds but often require purchase and carry access and privacy restrictions that differ from open repositories [8].
1. Where the open BIN lists live and what they contain
The most direct route to open BIN/IIN tables is community projects: the bin-list-data GitHub repository offers an open-source CSV with fields like card brand, type, country and issuer, published under Creative Commons Attribution 4.0 so researchers can reuse and redistribute it legally [1]; these repositories are typically lightweight "lookup" data useful for card-branding, geo-tagging and issuer analysis but do not include transaction-level flows or sensitive cardholder data [1].
2. Payments catalogues and general-purpose dataset platforms
Broader dataset platforms such as data.world host payment-related datasets and can serve as a hub to discover BIN lists among other payment resources [2], while aggregator directories and teaching resources point researchers toward free, open sources and explain licensing and access expectations [3]; these platforms help surface both community files and links to more specialized datasets but vary in curation and depth [2] [3].
3. Synthetic and simulated payment data for pattern analysis
When BIN-linked transaction data is unavailable for legal or privacy reasons, researchers commonly turn to synthetic datasets and simulators: PaySim and BankSim are widely used simulators that reproduce mobile-money and banking transaction patterns for fraud-detection research and have been published and curated on platforms like GitHub and Kaggle [4] [5] [6]; academic collections and community lists (e.g., AI4FCF open datasets) also provide tuned synthetic logs derived from aggregated real patterns for reproducible research [7].
4. Commercial datasets, limitations and hidden incentives
Commercial vendors and data marketplaces curate expansive electronic payment feeds and BIN-enriched products with multi-year histories and richer merchant and routing metadata, but these offerings are typically paid and contractually restricted; industry directories like Datarade summarize these providers and implicitly reveal a gatekeeping dynamic where higher-quality BIN-linked transaction data is monetized rather than openly shared [8].
5. What is not publicly available and where researchers should be cautious
Public, legal BIN lists and synthetic transaction sets exist, but true, anonymized transaction logs tied to live BINs remain scarce in the open domain for privacy and regulatory reasons — historical anonymized competitions (e.g., the Berka dataset noted in academic discussion) and curated challenge datasets exist but are limited and dated [9], and public government payment disclosures address other payment types (e.g., Open Payments) rather than BIN-linked card streams [10]. Researchers should therefore temper expectations: open BIN tables and simulators support many analytic tasks, while precise, node-level, real-world BIN-to-transaction datasets often sit behind vendor contracts or inside regulated institutions [8] [9].
6. Practical next steps for rigorous, legal research
Begin by downloading and licensing community BIN lists (e.g., GitHub bin-list-data under CC-BY) to establish issuer and scheme mapping, use curated platform searches on data.world and dataset directories to find complementary files [1] [2] [3], and employ PaySim/BankSim synthetic transaction generators or academic synthetic collections to model transaction flows and fraud scenarios where real data cannot be accessed legally [4] [5] [7]; when richer, real-world feeds are essential, budget for vetted commercial providers and be transparent about contractual and privacy constraints when publishing results [8].