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IndicBankBench

Tasks:
Text Generation
Languages:
English
Size:
n<1K
Tags:
benchmark
banking
tool-calling
function-calling
agents
india
License:
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metadata 
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:

Files and split

The release has one unsplit case bank:

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:

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:

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, 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. 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.