What banks should ask before letting AI clean their data

Before AI cleans bank data, ask three things: does it change source records or work on a copy, who approves each change, and can every change be traced and reversed. Banks carry regulatory obligations for the accuracy of their records, so a tool that fixes data without an approval trail can create more risk than it removes.

Data cleaning is valuable. In a bank, how it happens matters as much as what it fixes.

Questions to ask any vendor

1. Where do changes happen? In the source system, in a copy, or only in a mapping layer on top?
2. Proposed or applied? Does the AI suggest fixes for a person to accept, or apply them on its own?
3. Who approves? Is there a named owner for each type of change?
4. What's recorded? Is every change logged with the before and after values, the reason and the approver?
5. Can it be reversed? How do you undo a change that turns out to be wrong?
6. Is your data used for training? Will your records be used to train the vendor's models?
7. Who can see what? Does the tool respect the access permissions you already have?
8. Where is data processed? Does it run in your environment or the vendor's, and will it pass your third-party risk review?

Why banks are cautious

1. Record accuracy. Banks are accountable for the accuracy of what they report and file.

2. Exam scrutiny. Examiners ask how figures were produced, and an unexplained change is hard to defend.

3. AI errors. A confident but wrong fix can spread into every report that uses the record.

What good looks like

AI does the slow work of finding duplicates, mismatches and broken mappings. A person approves what changes. Every change is logged, reversible and tied to an owner.

How Preql helps

Preql's agents propose mappings, and business owners approve them. Every change is version-controlled with an audit trail. Preql inherits your existing access controls, uses role-based access and encryption, and doesn't train on customer data.

FAQ

Is AI data cleaning safe for regulated data?

It can be, when every change is proposed, approved, logged and reversible.

Who should approve changes?

The business owner of the data, not only the data team.