---
license: cc-by-4.0
pretty_name: IndicBankBench
task_categories:
  - text-generation
tags:
  - benchmark
  - banking
  - tool-calling
  - function-calling
  - agents
  - india
language:
  - en
size_categories:
  - n<1K
---

# IndicBankBench dataset

IndicBankBench is a 799-case benchmark for evaluating tool-using language-model assistants in
Indian retail banking. Each JSON case describes a synthetic, scripted, multi-turn interaction,
the available mock tools, the expected tool behavior, and staged grading criteria. It contains
no real customer records.

Anonymous-review locations:

- Dataset: https://huggingface.co/datasets/anonuser73268/IndicBankBench
- Dataset ID: `anonuser73268/IndicBankBench`
- Compatible code: https://github.com/anonuser73268/IndicBankBench

## Files and split

The release has one unsplit case bank:

```text
case_bank/<domain>/<tool>/<axis>.NNN.json
```

There are 799 JSON files and 799 unique `case_id` values. Cases are grouped by domain and
primary tool for navigation; those directories are not training, validation, or test splits.
File order is the lexicographic order of relative paths, which is also the order used by the
reference harness.

- `case_bank/`: canonical case JSON files
- `SCHEMA.md`: field and format documentation
- `meta.json`: count, layout, compatibility, and aggregate checksum metadata
- `VALIDATION.md`: release validation results and known linter findings
- `LICENSE`: CC BY 4.0 license notice and attribution guidance
- `DISCLAIMER.md`: intended-use and warranty disclaimer

## Load the cases

The files can be loaded with the Python standard library:

```python
import json
from pathlib import Path

root = Path("case_bank")
cases = [json.loads(path.read_text(encoding="utf-8"))
         for path in sorted(root.rglob("*.json"))]
assert len(cases) == 799
assert len({case["case_id"] for case in cases}) == 799
```

To use the reference harness from a sibling checkout:

```bash
cd ../code
export INDICBANKBENCH_DATA=../data
python -m unittest discover -s indicbankbench/tests
python indicbankbench/scripts/case_lint.py
```

## Dataset characteristics

- Language: English
- Modality: scripted multi-turn text interactions plus structured mock-tool data
- Domains: five operational banking domains plus capability/refusal cases
- Tools: 32 mock banking tools
- Primary evaluation axes: 20
- Personal data: synthetic fixtures only

The dataset is intended for controlled research, testing, and benchmarking. It must not be used
for live banking, customer, legal, regulatory, credit, fraud, or financial decisions.

## Provenance

The cases and bank-specific fixture fields are synthetic. Names and selected demographic
descriptors used when constructing the synthetic login contexts were derived from
[NVIDIA Nemotron-Personas-India](https://huggingface.co/datasets/nvidia/Nemotron-Personas-India),
which is licensed under CC BY 4.0. Bank-specific identifiers, masked contact fields, addresses,
dates, and relationship fields were generated synthetically.

## License and citation

The case data and authored dataset documentation are licensed under Creative Commons Attribution
4.0 International; see [LICENSE](LICENSE). Evaluation results and model or judge outputs are not
part of this dataset release. During anonymous review, cite the accompanying anonymous paper
rather than attempting to identify its authors. Final citation metadata will be added after
review.