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license: cc-by-4.0
pretty_name: IndicBankBench
task_categories:
- text-generation
tags:
- benchmark
- banking
- tool-calling
- function-calling
- agents
- india
- arxiv:2609.29167
language:
- en
size_categories:
- n<1K
configs:
- config_name: default
data_files:
- split: test
path: preview/cases.jsonl
---
# IndicBankBench — A Benchmark for Evaluating the Safety and Reliability of Language Models in Indian Retail Banking
[📄 Paper](https://arxiv.org/abs/2609.29167) · [Code](https://github.com/npci/IndicBankBench)
IndicBankBench evaluates whether language models behave safely and reliably in Indian
retail-banking interactions. Its 799 synthetic, multi-turn cases test grounding in customer
context, safe action-taking with mocked banking tools, appropriate clarification and refusal,
and complete resolution of customer requests. All data is synthetic and contains no real customer
information.
## Layout
Each case is a JSON file at `case_bank/<domain>/<tool>/<axis>.NNN.json`. Key fields: `case_id`,
`axis`, `domain`, `tool`, `target_behavior`, `setup` (persona + `login_context` +
`prior_messages`), `tools_exposed`, `user_turns`, `mock`, `gold`, and `grading`.
The Dataset Viewer reads the generated `preview/cases.jsonl`; the harness uses the canonical JSON
case files.
## Running with the harness
Clone the [IndicBankBench](https://github.com/npci/IndicBankBench) harness, then download the
cases and run:
# Clone and install
git clone https://github.com/npci/IndicBankBench.git
cd IndicBankBench
python3 -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install -e .
# Copy the configuration template
cp .env.example .env
# Download the case data
hf download NPCI/IndicBankBench --repo-type dataset --local-dir ./data
export INDICBANKBENCH_DATA=./data
# Run an evaluation
python -m harness.cli run --run-id my_model_v1
Set the candidate and judge endpoints in `.env`. See `meta.json` in this repo for the checksums
of this snapshot, including the content-only checksum recorded by the harness.
## Scoring model
Every case is graded in four phases — **S** (safe), **A** (actions), **R** (response), **Q**
(quality) — a case passes only if every active S, A, and R gate holds. S and A are deterministic
code checks; R is the single LLM-judged gate.
## Citation
```bibtex
@misc{paul2026indicbankbench,
title = {IndicBankBench: Evaluating Safety and Reliability of Language Model Assistants in Indian Retail Banking},
author = {Suvradip Paul and Chandra Bhushan and Harsh Sharma and Nitin Kukreja and Yatharth Dedhia and Keyur Doshi and Prashant Devadiga},
year = {2026},
eprint = {2609.29167},
archivePrefix = {arXiv},
primaryClass = {cs.AI},
doi = {10.48550/arXiv.2609.29167},
url = {https://arxiv.org/abs/2609.29167}
}
```
## License
The case data is licensed under CC BY 4.0 (see `DATA_LICENSE.md`). See
`DISCLAIMER.md` for the full disclaimer.
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