Public dataset for account to account payments

Question

I'm looking for a dataset which contains account to account payments (bank transfers). Ideally, this dataset would contain labeled data for transactions or accounts known to be victims of phishing attacks. In this scenario, the account holder enters and authenticates the transfer but has been tricked into making an undesired purchase or into sending the funds to an undesired recipient.

This could be a public dataset, or optionally I could collaborate confidentially on a private dataset and would sign the necessary confidentiality agreements.

I've looked at the repositories listed here already: Publicly Available Datasets

I do know this credit card fraud dataset well, and it's the closest to what I'm searching for, but does not fill the requirements above: https://www.kaggle.com/mlg-ulb/creditcardfraud

For experts in this area, a more technical way to describe this fraud scenario is "authorized push payments".

Answer 1

There is this dataset and if it doesnt match, maybe you could contact the authors at Brussels University for more information.

https://data.world/raghu543/credit-card-fraud-data

The datasets 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.

This one uses simulated data but sounds more like what you are looking for. There is also an academic paper that goes with it.

https://www.kaggle.com/ntnu-testimon/banksim1

We ran BankSim for 180 steps (approx. six months), several times and calibrated the parameters in order to obtain a distribution that get close enough to be reliable for testing. We collected several log files and selected the most accurate. We injected thieves that aim to steal an average of three cards per step and perform about two fraudulent transactions per day. We produced 594643 records in total. Where 587443 are normal payments and 7200 fraudulent transactions. Since this is a randomised simulation the values are of course not identical to original data.

Answer 2

Regarding bank payments, no that much datasets are publicly available. You can have a look at customer complaint databases like the CFPB one where some of the complaints related to bank transfers (Money transfer, virtual currency, or money service (check cashing service, currency exchange, cashier's/traveler's check), are catalogued as Fraud or scam:

The Consumer Complaint Database is a collection of complaints about consumer financial products and services that we sent to companies for response. Complaints are published after the company responds, confirming a commercial relationship with the consumer, or after 15 days, whichever comes first. Complaints referred to other regulators, such as complaints about depository institutions with less than $10 billion in assets, are not published in the Consumer Complaint Database. The database generally updates daily.

Regarding credit card, you can also use this dataset available (after registration) at the "European Data Incubator" website:

Existing fraud prevention mechanism in banks are mostly based on manpower-based rules. These rules evaluate the fraud risk of credit card transactions and inform the fraud operation team according to the risk scores of rules. This process is daily reported to the credit cardholders by operation team by considering the daily call capacity. This challenge is about forecasting fraud transactions of credit card users of YKB with machine learning technics instead of traditional rule-based systems. Credit card fraud means a transaction that is not intentionally performed by the card holder.

Dataset Description: The dataset is anonymized with PCA method and balanced at card level to reduce the high-class imbalance. Half of these credit cards are selected based on the criteria of having at least one fraudulent transaction in the given time frame. Accordingly, the remaining half consist of credit cards that do not have any fraudulent transaction in the time frame. This dataset can be used to train models but should not be used to evaluate their performance. For the evaluation please use the unbalanced dataset that is also provided.

Balanced Training Dataset Time of the transaction -- Datetime Amount of the transaction -- number Unique anonymized card identifier Label - 1: fraud, 0: legitimate

Unbalanced Test Dataset Time of the transaction -- Datetime Amount of the transaction -- number Unique anonymized card identifier Label - 1: fraud, 0: legitimate