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