Public dataset for account to account payments 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 &quot;authorized push payments&quot;.
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.