Datasets:

NPCI
/

IndicBankBench

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 · Code

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 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

@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.