AI in banking: five practical uses for teams
Where banking teams can start using AI for support tasks and how to organise an initial trial.
AI in banking can initially be explored for support tasks such as summarising documents, drafting training materials and preparing communications. These uses should be distinguished from financial decisions that directly affect customers.
This article proposes exercises and processes for internal evaluation. It is not legal or financial guidance, and using AI does not automatically authorise data processing. Every trial should follow the institution's requirements for tools, permissions and controls.
Summarising internal procedures
An employee can request a summary of an approved procedure while retaining references to the source sections. The purpose is to identify the steps and information that require further reading, rather than create an alternative version of the procedure.
Test the output with questions involving exceptions and special cases. If a summary removes an important condition, record it as an error even when the rest of the text is clear.
Preparing training materials
Approved material can be used to create learning scenarios, knowledge checks and role-specific summaries. Use synthetic cases and verify that quiz answers match the procedure.
A training team can assess a draft by asking whether the questions test understanding or simply recall. A practical situation where an employee must choose the next step may be more useful than a question that repeats a paragraph heading.
Drafting internal communications
AI can help adapt an announcement for different audiences within an institution. The source material should contain the facts, date and required action. Final approval remains with the responsible person.
For example, an announcement about a new process could become a short email and a set of frequently asked questions. Check both versions against the same source.
Exploring feedback themes
In an exercise using synthetic or approved comments, AI can suggest recurring themes. The team checks the examples within each theme and records cases that do not fit. Classification should not be confused with a complete analysis of causes.
If several comments mention long waiting times, that is a topic to investigate, not proof of the cause. Conclusions need to be connected to other data and the team's assessment.
An assistant for approved information
An internal assistant can be designed around a limited set of documents. Before expanding it, check whether users receive only information they are allowed to see and whether the response identifies its source.
Outdated content is another point to check. When a procedure changes, update the source and repeat the test questions affected by the change.
How to organise an initial trial
Choose a small team, one task and a set of approved materials. Assign responsibility for reviewing results and recording errors. Before measuring speed, define the quality required.
Use the same cases to compare the current process with the trial process. Include review time and cases that return to manual handling. The results apply to that trial; they do not automatically establish the safety of other uses.
Frequently asked questions
Can real customer data be used in training?
Use synthetic materials or materials explicitly approved for that environment. Data and tool approval should come from the bank's responsible functions.
What is the first step for a manager?
Identify a focused need and agree on the trial criteria with the relevant teams.
Prepare your team for practical use
Lumina offers training tailored to organisational roles and needs. Let's discuss a programme that connects AI knowledge with your team's support tasks.