Triage incoming messages
Suggest the intent that best matches your team’s support taxonomy.
STAYCHARTED AMT / BANKING AND FINTECH
Classify banking and fintech customer messages
Learn intent categories from labeled customer messages to help support teams route work and review uncertain cases.
FROM YOUR EXAMPLES
My replacement card has still not arrived.Card deliveryIllustrative category · Not a live prediction
Where this fits in your work
Organize historical feedback
Label exported messages to help analysts review topics across a file.
Support queue routing
Pass suggested intents to your existing routing logic, with a review path for uncertain cases.
PREPARE YOUR EXAMPLES
Start with a file your team has already labeled.
Pair historical messages with the intent your team assigned: a missing card, a transfer question, or a disputed charge. These are examples of a taxonomy, not decisions about a customer’s eligibility, credit, or access to funds.
Use one row per example with an approved answer. Keep the input representative of what the model will receive later, and agree on how to label ambiguous cases before training.
| Customer message | Approved intent |
|---|---|
| My replacement card has not arrived. | Card delivery |
| I do not recognize this card payment. | Unrecognized card payment |
| Why is my transfer still pending? | Pending transfer |
Illustrative rows and categories. Use your own approved labels.
From examples to reviewed results
Prepare
Resolve inconsistent labels and remove unnecessary sensitive information.
Train
Choose a model on your plan and train on representative examples.
Validate
Inspect held-out accuracy by category and the mistakes that remain.
Use and improve
Publish, fill a file or call the API, then review and feed corrections into the next version.
KEEP REVIEW IN THE WORKFLOW
Know where the model stops.
Review ambiguous messages, urgent requests, and any result used in a consequential action. Intent classification is not fraud detection, identity verification, lending, or an authorization to move money.
Evidence and implementation
Prepare and check the data
Remove information that is not needed for the classification. Review detected personal information before training, and check labels where similar wording belongs to different intents. Train on representative messages and examine accuracy for each category.
Read the BANKING77 evidence
The published BANKING77 benchmark reports 93.7% held-out test accuracy for the fine-tuned model on 77 banking intents. That is a result on a public benchmark, not a promised score for your customer messages. The article compares approaches and shows how confidence thresholds affect the amount of review.
Route with review built in
Classify new messages through files or the API and use the result as an input to your support workflow. Review uncertain and high-impact cases before acting. Add corrected examples when products, terminology, or policies change, then test the next model version.
File fills return category suggestions for your existing import process. API calls return results to your own software; your integration decides how to apply them. Plan allowances and workspace rate limits still apply.
Common questions
Is this a banking decision engine?
No. It categorizes message text. Keep eligibility, account access, payment actions, and other consequential decisions in your existing controlled workflows.
How should we handle personal data?
Use only authorized data, remove unnecessary sensitive details, and review the supported masking checks before training. The checks do not detect every kind of sensitive information.
Can one message have several intents?
The classification workflow returns a category. Define how your team labels multi-topic examples and route ambiguous cases for review.
Explore the details
Try it on your own examples.
Start with Free for text classification. Compare plans for AI models, Vision, and API access.