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BhashaBench-Finance (BBF): Benchmarking AI on Indian Financial Knowledge

Overview

BhashaBench-Finance (BBF) is the first comprehensive benchmark designed to evaluate AI models on Indian financial knowledge and practices. Tailored for India's diverse financial ecosystem, regulatory framework, and economic landscape, BBF draws from 25+ official government and institutional financial exams to assess models' ability to provide accurate, policy-compliant, and contextually relevant financial advice and analysis.

Key Features

• Languages: English and Hindi (with plans for more Indic languages)
• Exams: 25+ unique financial government and institutional exams across India
• Domains: 30+ financial and allied domains, spanning comprehensive financial knowledge
• Questions: 19,433 rigorously validated, exam-based questions
• Difficulty Levels: Easy (7,111), Medium (9,348), Hard (2,974)
• Question Types: Multiple Choice, Assertion-Reasoning, Match the Column, Rearrange the Sequence, Fill in the Blanks, Reading Comprehension, Essay
• Focus: Practical, regulation-aware, India-specific financial knowledge essential for financial professionals and consumers

Dataset Statistics

Metric	Count
Total Questions	19,433
English Questions	13,451
Hindi Questions	5,982
Subject Domains	30+
Government Exams Covered	25+

Dataset Structure

Test Set

The test set consists of the BhashaBench-Finance (BBF) benchmark, which contains approximately 19,433 questions across 2 Indic languages (English and Hindi).
We will add support for more Indic languages in upcoming versions.

Subject Domains in BBF

Subject Domain	Count
Problem Solving	5,686
Mathematics for Finance	4,845
Banking Services	1,171
Governance & Policy	1,064
Language & Communication	946
Corporate Finance & Investment	910
Commerce	863
Accounting	773
General Knowledge	539
Information Technology Finance	490
Economics & Development Studies	274
Rural Economics	261
Environmental Finance	168
Taxation & Regulatory Compliance	155
Interdisciplinary Finance	153
Data & Analytics in Finance	127
History, Sociology & Cultural Studies of Finance	127
Finance Education	118
Healthcare Economics	114
Science and Technology in Finance	101
International Finance & Trade	83
Business Management	83
Energy, Infrastructure & Finance	82
Behavioral Finance	67
Financial Markets	47
Sports, Media & Finance Linkages	45
Marketing Finance	42
Insurance & Risk Management	42
Legal Finance	34
Financial Technology	23

Question Distribution

By Difficulty Level

Level	Count	Percentage
Easy	7,111	36.6%
Medium	9,348	48.1%
Hard	2,974	15.3%

By Question Type

Type	Count	Percentage
MCQ	18,019	92.7%
Rearrange the sequence	708	3.6%
Fill in the blanks	286	1.5%
Assertion or Reasoning	215	1.1%
Match the column	119	0.6%
Reading Comprehension	86	0.4%

Usage

Since this is a gated dataset, after your request for accessing the dataset is accepted, you can set your HuggingFace token:

export HF_TOKEN=YOUR_TOKEN_HERE

To load the BBF dataset for a Language:

from datasets import load_dataset
language = 'Hindi'
# Use 'test' split for evaluation
split = 'test'
language_data = load_dataset("bharatgenai/BhashaBench-Finance", data_dir=language, split=split, token=True)
print(language_data[0])

Evaluation Results Summary

• 40+ models evaluated, including GPT-based models, Gemma, Llama, Qwen, and specialized financial AI systems.

• Language Performance Gap:

  • Consistent 5-7% performance drop from English to Hindi across most models
  • Best Hindi performance still significantly below English benchmarks

• Domain Performance Insights:

  • Strongest domains (70%+ by top models):
    • Information Technology Finance (92.24%)
    • Business Management (84.34%)
    • History, Sociology & Cultural Studies of Finance (83.46%)
  • Most challenging domains (<50% by top models):
    • Problem Solving (47.12%)
    • Mathematics for Finance (58.04%)
    • Legal Finance (76.47%)

• Difficulty Level Analysis:

  • Easy questions: Top models achieve 72-73% accuracy
  • Medium questions: Performance drops to 59% for best models
  • Hard questions: Significant challenge with only 40-41% accuracy

• Question Type Performance:

  • Best: Fill in the blanks (81.82% top performance)
  • Moderate: MCQ (61.7%), Assertion/Reasoning (67.91%)
  • Challenging: Rearrange sequence (49.01%), Match column (69.75%)

• Model Size vs Performance:

  • Larger models (Qwen3-235B, DeepSeek-v3) significantly outperform smaller models
  • Specialized financial fine-tuning shows mixed results compared to general large models

For detailed results and analysis, please refer to our blog.

Applications

BBF enables evaluation of AI systems for:

• Financial advisory services in Indian context
• Banking and insurance customer support systems
• Regulatory compliance automation tools
• Financial education platforms for Indian markets
• Investment analysis systems understanding Indian financial instruments

Citation

Please cite our benchmark if used in your work:

@misc{bhashabench-finance-2025,
  title  = {BhashaBench-Finance: Benchmarking AI on Indian Financial Knowledge},
  author = {BharatGen Research Team},
  year   = {2025},
  howpublished = {\url{https://huggingface.co/datasets/bharatgenai/bhashabench-finance}},
  note   = {Accessed: YYYY-MM-DD}
}

License

This dataset is released under the CC BY 4.0.

Contact

For any questions or feedback, please contact:

• Vijay Devane ([email protected])
• Mohd. Nauman ([email protected])
• Bhargav Patel ([email protected])
• Kundeshwar Pundalik ([email protected])

Links

• GitHub Repository 💻
• Paper 📄