Money matters affect everyone, from a college student opening their first bank account to a seasoned entrepreneur working through complex tax rules. Yet reliable, India-specific financial guidance is still hard for millions to find. AI models trained mainly on Western financial systems struggle with Indian regulatory frameworks, cultural contexts and the realities of our diverse economy.
FinanceParam is our specialised 2.9 billion parameter model for Indian finance, fine-tuned from Param-1-2.9B-Instruct. It covers personal budgeting, tax planning, banking, investments and policy analysis, in both English and Hindi.
Built for financial complexity: architecture
FinanceParam uses the Param-1-2.9B-Instruct architecture and is adapted for financial questions through fine-tuning.
Technical specifications:
- Parameters: 2.9 billion, fine-tuned for the financial domain.
- Architecture: 32-layer transformer with grouped-query attention for efficient processing.
- Context window: 2,048 tokens.
- Attention design: 16 heads with 8 key-value heads to reduce memory use.
- Activation: SiLU throughout the network.
- Positional embeddings: RoPE with theta=10,000.
- Training precision: bf16-mixed for computational efficiency.
- Vocabulary: 256,000 tokens.
The architecture balances computational efficiency with the analytical depth that financial questions need, from personal budgeting to the effects of policy across economic sectors.
Assembling financial knowledge: dataset construction
Building FinanceParam involved systematic curation from authoritative sources, guided question generation and adaptation to Indian financial practices.
Data foundation:
- Core sources: 25,000+ finance-focused passages from trusted Indian sources.
- Question generation: For each passage, an open-weight LLM generated five question-answer pairs covering personal finance, taxation, banking, insurance and investments.
- Expanded dataset: 9 million question-answer pairs grounded in a finance taxonomy and user personas, with the full set translated into Hindi.
- Conversation data: 8 million multi-turn dialogues for complex financial scenarios.
- Training volume: 24 million samples in total.
Source diversity:
Our training data draws from across the Indian financial landscape:
- Government portals: Official documentation from the Income Tax Department, RBI, SEBI and IRDAI.
- Institutional reports: Banking sector analysis, investment guidelines and regulatory updates.
- Policy documents: Economic policies, financial reforms and sector-specific regulations.
- Advisory content: Investment guidance, personal finance strategies and market analysis.
- News sources: Financial journalism covering Indian market developments.
Knowledge taxonomy:
The training data is organised around the needs of users such as salaried professionals, students, investors, small business owners, retirees and policymakers:
- Personal finance: Budgeting, savings, debt management and financial planning.
- Taxation: Income tax, GST, corporate taxation and compliance requirements.
- Banking: Account management, loans, digital payments and banking services.
- Investments: Mutual funds, stocks, bonds, insurance and retirement planning.
- Business finance: Corporate finance, startup funding, business loans and accounting.
- Policy analysis: Economic policies, regulatory changes and market implications.
Training for financial reasoning
FinanceParam was trained with supervised fine-tuning, using templates designed for financial consultation patterns.
Training configuration:
- Foundation model: Param-1-2.9B-Instruct.
- Framework: Transformers with PyTorch multi-node distribution.
- Training scale: 24 million samples over one epoch.
- Learning strategy: Linear scheduler with a 2e-4 learning rate.
- Batch processing: 512 samples per batch.
Finance-specific enhancements:
- Custom prompt templates designed for Indian financial consultation patterns.
- Multi-turn conversation capabilities for complex financial planning scenarios.
- Bilingual consistency, so financial concepts carry over accurately between English and Hindi.
Performance analysis: BhashaBench‑Finance results
Evaluating financial AI needs benchmarks that capture the complexity and specificity of Indian financial systems. FinanceParam is evaluated on BhashaBench-Finance (BBF), our benchmark for Indian financial knowledge. You can read more in our BhashaBench-Finance write-up.
Benchmark scope:
- Question volume: 19,433 validated questions from 25+ official financial examinations.
- Source authority: Government financial services exams, banking assessments and institutional certifications.
- Domain coverage: 30+ financial disciplines, from personal finance to policy analysis.
- Language support: 13,451 English and 5,982 Hindi questions.
- Difficulty levels: Easy (7,111), Medium (9,348) and Hard (2,974) questions.
Overall performance:
FinanceParam achieves competitive results on BBF:
- Combined BBF score: 31.42%.
- English performance: 32.24%.
- Hindi performance: 29.56%.
- Bilingual gap: 2.68 points, smaller than every compared model except Llama-3.2-1B-Instruct.
Competitive positioning:
How FinanceParam compares with general-purpose models of similar size:
- Qwen2.5-3B-Instruct: 33.09% (1.67 points ahead of FinanceParam, the top score in this group).
- Llama-3.2-3B-Instruct: 31.76% (0.34 points ahead).
- Granite-3.1-2B-Instruct: 31.07% (FinanceParam is 0.35 points ahead).
- Gemma-2-2B-IT: 30.24% (FinanceParam is 1.18 points ahead).
- Llama-3.2-1B-Instruct: 26.21% (FinanceParam is 5.21 points ahead).
Domain strengths:
FinanceParam scores highest among the compared models in these domains:
- Marketing Finance: 61.90% accuracy.
- Behavioural Finance: 47.76% accuracy.
- Rural Economics: 47.13% accuracy, an important area for India.
- Environmental Finance: 45.83% accuracy.
- International Finance and Trade: 45.78% accuracy.
- History, Sociology and Cultural Studies of Finance: 45.67% accuracy.
Performance by complexity:
- Easy questions: 38.31% accuracy.
- Medium questions: 27.71% accuracy.
- Hard questions: 26.60% accuracy, the highest among the compared models.
Where FinanceParam needs work:
FinanceParam is weaker on Mathematics for Finance (25.59%) and Problem Solving (25.71%). Together these two domains make up more than half of BBF's questions, so improving numerical reasoning is a priority for future versions.
Improving financial access in India
Financial literacy and access to quality financial advice remain significant challenges across India. The National Centre for Financial Education's 2019 survey found that only about 27% of Indian adults were financially literate. FinanceParam is designed to help close these gaps.
Widening access to financial knowledge:
- Universal access: Financial guidance available regardless of location or economic status.
- Language inclusion: Hindi support that lowers the language barrier to financial information.
- Cultural relevance: Guidance grounded in Indian financial practices and challenges.
- Cost: Access to financial information at minimal cost.
- Educational support: Interactive learning for students, professionals and lifelong learners.
Practical applications:
FinanceParam supports use cases across Indian financial services:
- Personal financial planning: Budgeting, investment information and long-term goal setting.
- Tax assistance: Income tax guidance, GST compliance and tax planning.
- Banking support: Account management, loan applications and digital payments.
- Investment guidance: Mutual fund basics, stock market basics and retirement planning.
- Business finance: Startup funding, business loan processes and financial management.
- Policy understanding: Economic policy impacts, regulatory changes and market implications.
The vision ahead:
FinanceParam is our first step towards comprehensive financial AI for India. Future work will incorporate real-time market data, expand regional language support and improve performance in specialised domains. Our goal is a financial AI ecosystem that serves every Indian, from rural farmers accessing microfinance to urban professionals managing complex portfolios.
As India's digital financial services keep expanding, FinanceParam helps make sure AI-powered financial guidance reflects our regulatory environment and economic realities. The aim is to use AI to strengthen financial inclusion for every Indian.
Resources:
- Model access: FinanceParam on Hugging Face.
- Evaluation benchmark: BhashaBench-Finance.
- Technical documentation: Available in the model repository.
