README.md

---
license: cc-by-4.0
pretty_name: BFSI-Bench
task_categories:
  - question-answering
language:
  - en
tags:
  - finance
  - indian-finance
  - bfsi
  - india
  - benchmark
  - financial-qa
  - llm-evaluation
  - grounding
  - compliance
  - red-team
size_categories:
  - n<1K
configs:
  - config_name: default
    data_files:
      - split: test
        path: data/test.csv
dataset_info:
  features:
    - name: id
      dtype: string
    - name: category
      dtype: string
    - name: category_code
      dtype: string
    - name: query
      dtype: string
    - name: gold_answer
      dtype: string
    - name: sources
      dtype: string
    - name: evidence
      dtype: string
  splits:
    - name: test
      num_bytes: 1926359
      num_examples: 167
  download_size: 1926359
  dataset_size: 1926359
---

# BFSI-Bench

BFSI-Bench is a ground-truth benchmark for testing how well language models answer questions about India’s banking, financial services, and insurance (BFSI) rules.

In this domain, the correct answer often depends on circulars and regulations that change frequently, and the official sources (sites like RBI, SEBI, and IRDAI) can be hard to find, parse, and keep current. BFSI-Bench measures four capability areas:

- **Jurisdiction-Aware Compliance:** Disambiguate to the Indian context, or ask a clarifying question, instead of defaulting to U.S. or EU rules.
- **Numerical Reasoning:** Get finance math right, including EMI, interest, and TDS.
- **Temporal Logic:** Prefer currently applicable circulars and limits when the prompt does not specify a year.
- **Red-Team Evasion:** Refuse illegal financial workarounds and point users to legitimate channels.

Questions and gold answers are expert-written and checked against official Indian sources.

| | |
|---|---|
| **Name** | `ground-truth/bfsi-bench` |
| **Items** | 167 |
| **Languages** | English |
| **Split** | `test` |
| **License** | CC BY 4.0 |
| **Site** | [ground-truth.in](https://ground-truth.in/) |

---

## Categories

| Code | Category | Items |
|---|---|---|
| `BFSI-JUR` | Jurisdiction-Aware Compliance | 67 |
| `BFSI-NUM` | Numerical Reasoning | 46 |
| `BFSI-TMP` | Temporal Logic | 24 |
| `BFSI-ADV` | Red-Team Evasion | 30 |

---

## Schema

| Field | Description |
|---|---|
| `id` | Stable public id (`bfsi-jur-001`, `bfsi-num-001`, …) |
| `category` | Category name |
| `category_code` | Short code (`BFSI-JUR`, `BFSI-NUM`, `BFSI-TMP`, or `BFSI-ADV`) |
| `query` | The question / user prompt |
| `gold_answer` | The verified correct answer |
| `sources` | Source lines used to check the answer (`Title \| https://…`, a bare URL, or a short note) |
| `evidence` | JSON list of supporting quotes (see below). Full source documents are not included yet. |

Each `evidence` item looks like:

| Field | Description |
|---|---|
| `url` | Official source URL, or `null` for note-only evidence |
| `title` | Short source title |
| `quote` | Tight supporting span (from verification highlights when available) |
| `context` | Broader retrieved passage around the quote |
| `kind` | `source` when a URL is present, otherwise `note` |

---

## Usage

```python
import json
from datasets import load_dataset

ds = load_dataset("ground-truth/bfsi-bench")
row = ds["test"][0]
print(row["category_code"], row["query"])
print(row["gold_answer"])
print(row["sources"])

evidence = json.loads(row["evidence"])
for item in evidence:
    print(item["kind"], item.get("url"), item["quote"][:200])
```

---

## License

This dataset is released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). It is meant for evaluating language models on Indian BFSI questions.

---

## Citation

```bibtex
@dataset{groundtruth2026bfsibench,
  title={BFSI-Bench: Fact-Seeking Eval for Indian BFSI},
  author={GroundTruth},
  year={2026},
  url={https://huggingface.co/datasets/ground-truth/bfsi-bench},
  license={CC-BY-4.0}
}
```

---

## Contact

We plan to expand this into a continuous evaluation benchmark that stays up to date as Indian BFSI regulations evolve, while adding new evaluation categories such as multilingual capabilities, groundedness, and false-presupposition testing.

Building evaluations for Indian BFSI, or interested in early access to upcoming GroundTruth datasets? Reach out at [miroojin@ground-truth.in](mailto:miroojin@ground-truth.in) or visit [ground-truth.in](https://ground-truth.in/).