learn-abc/banking-intent-dataset

Multilingual Banking Intent Dataset

Dataset Overview

This dataset is a custom-built multilingual intent classification dataset designed for banking chatbot systems. It supports English, Bangla (Bengali script), and Banglish (Romanized Bengali), with limited code-mixed examples.

The dataset was created for training production-grade multilingual banking intent classifiers with strong out-of-domain fallback detection.

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Dataset Size

• Total Samples: 134,412 original samples
• Additional Code-Mixed Samples: 500
• Augmented Training Samples: 114,218
• Test Samples: 20,194

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Supported Languages

Language	Count
English	33,657
Bangla	33,657
Banglish	33,657
Code-Mixed	500

The dataset maintains balanced distribution across the three primary languages.

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Task

• Text Classification
• Domain: Banking Intent Classification
• Number of Labels: 14

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Label List

ACCOUNT_INFO
ATM_SUPPORT
CARD_ISSUE
CARD_MANAGEMENT
CARD_REPLACEMENT
CHECK_BALANCE
EDIT_PERSONAL_DETAILS
FAILED_TRANSFER
FALLBACK
FEES
GREETING
LOST_OR_STOLEN_CARD
MINI_STATEMENT
TRANSFER

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Dataset Structure

Each row contains:

Column	Type	Description
text	string	User query
custom_intent	string	Intent label
language	string	Language tag (en, bn, bn-latn, code-mixed)

Example:

{
  "text": "amar account e koto taka ache",
  "custom_intent": "CHECK_BALANCE",
  "language": "bn-latn"
}

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Data Generation Methodology

The dataset was constructed using:

• Synthetic generation via large language models
• Manual intent definition and control
• Hard negative sampling
• Adversarial fallback enrichment
• Multilingual translation and normalization
• Stratified splitting
• Weighted balancing

Special attention was given to:

• Avoid keyword leakage
• Prevent cross-intent ambiguity
• Strengthen FALLBACK safety coverage
• Include conversational, short, long, formal, informal, and typo-based queries

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Intent Distribution

Intent	Distribution
FALLBACK	10782 ( 9.44%)
CARD_MANAGEMENT	10212 ( 8.94%)
FAILED_TRANSFER	10212 ( 8.94%)
ATM_SUPPORT	9920 ( 8.69%)
FEES	9789 ( 8.57%)
MINI_STATEMENT	9065 ( 7.94%)
CARD_REPLACEMENT	7864 ( 6.89%)
TRANSFER	7672 ( 6.72%)
CARD_ISSUE	7402 ( 6.48%)
EDIT_PERSONAL_DETAILS	7242 ( 6.34%)
CHECK_BALANCE	6916 ( 6.06%)
LOST_OR_STOLEN_CARD	6614 ( 5.79%)
ACCOUNT_INFO	5656 ( 4.95%)
GREETING	4872 ( 4.27%)

The FALLBACK class intentionally has higher representation for production safety.

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Intended Use

• Training multilingual banking chatbots
• Intent routing systems
• Financial conversational AI
• Indic language NLP research
• Multilingual text classification benchmarking

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Out-of-Scope Use

• Not designed for:

  • Sentiment analysis
  • Named entity recognition
  • Fraud detection
  • General conversational AI

• Not suitable for legal or financial advisory decisions

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Strengths

• Balanced multilingual coverage
• Production-oriented fallback handling
• Hard-negative enriched
• Adversarial robustness
• Banking-domain specific

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Limitations

• Synthetic data bias may exist
• Code-mixed examples limited compared to core languages
• Does not include multi-turn conversational context
• Performance may vary for extremely short ambiguous queries

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Ethical Considerations

• Dataset contains no personal user data
• No real customer information
• Fully synthetic and anonymized
• Designed to minimize unsafe financial action routing

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Evaluation Benchmark (Using MuRIL Base)

Model trained on this dataset achieved:

• Accuracy: 99.36%
• F1 Macro: 99.21%
• Strong multilingual generalization
• Robust fallback detection

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Citation

If you use this dataset, please cite:

• Author: Pursuit Software - Banking NLP Research
• Year: 2026
• Domain: Multilingual Banking Intent Classification

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Dataset Sources

• Original: Banking77 dataset (PolyAI) (Some of the intents are ignored in the final dataset)
• Synthetic + Mulitlingual: Generated my own new examples for important intents including FALLBACK, GREETING, and underrepresented intents and added to banking77 then translated that into bangla + banglish languages

Citation

If you use this dataset, please cite:

@dataset{banking_intent_2026,
  title={Banking Intent Classification Dataset},
  author={Abhishek Singh},
  year={2026},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/learn-abc/banking-intent-dataset}
}

License

This dataset is licensed under the MIT License.

Contact Me

For any inquiries or support, please reach out to:

• Author: Abhishek Singh
• LinkedIn: My LinkedIn Profile
• Portfolio: Abhishek Singh Portfolio

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