A machine learning model for classifying banking customer-support messages into their corresponding customer intent.
This project demonstrates a complete machine-learning workflow, including dataset inspection, data cleaning, train/validation/test splitting, model training, evaluation, error analysis, and prediction.
Project Overview
Customer-support systems receive a large number of messages every day. Automatically identifying the intent behind each message can help route customer queries to the correct support workflow.
This model takes a customer's message as input and predicts one of 77 banking-related customer-support intents.
Example
Input:
My card hasn't arrived yet
Prediction:
card_arrival
Another example:
Input:
I want to cancel my transfer
Prediction:
cancel_transfer
Task
Text Classification / Customer Intent Classification
The model performs multi-class classification on customer-support messages.
Input
A natural-language customer-support message.
Output
One of 77 predefined banking customer-support intents.
Dataset
The model was trained using the Banking77 dataset.
Banking77 contains banking-related customer queries categorized into 77 different intents.
The dataset was processed through the following pipeline:
This type of intent-classification model can be used as a component of:
Banking customer-support systems
Customer-service chatbots
Automated ticket routing
Support ticket classification
Customer-support analytics
FAQ and help-desk automation
Intent detection systems
The model itself is a classification component and can be integrated into a larger customer-support or chatbot system.
Limitations
This model was trained and evaluated using the Banking77 dataset.
Its performance on real-world customer messages may differ from the reported test performance.
The model should not be used as a production banking decision system without additional:
domain-specific validation
security testing
monitoring
bias evaluation
robustness testing
production testing
human oversight where required
The model is intended primarily as a machine-learning project and portfolio demonstration.
Future Improvements
Possible future improvements include:
Improving classification accuracy
Performing hyperparameter tuning
Adding confidence scores
Handling unknown or out-of-domain queries
Improving error handling
Adding more real-world customer-support data
Comparing multiple ML algorithms
Deploying the model as an API
Integrating the classifier with a conversational AI system
Adding monitoring and model evaluation pipelines
Project
Customer Support AI — Banking Intent Classification
This project demonstrates an end-to-end machine-learning workflow from raw customer-support data to a trained and evaluated classification model and an interactive prediction application.
