An Enterprise AI Analysis
FiMI: A Domain-Specific Language Model for Indian Finance Ecosystem
We present FiMI (Finance Model for India), a domain-specialized financial language model developed for Indian digital payment systems. We develop two model variants: FiMI Base and FiMI Instruct. FiMI adapts the Mistral Small 24B architecture through a multi-stage training pipeline, beginning with continuous pre-training on 68 Billion tokens of curated financial, multilingual (English, Hindi, Hinglish), and synthetic data. This is followed by instruction fine-tuning and domain-specific super-vised fine-tuning focused on multi-turn, tool-driven conversations that model real-world workflows, such as transaction disputes and mandate lifecycle management. Evaluations reveal that FiMI Base achieves a 20% improvement over the Mistral Small 24B Base model on finance reasoning benchmark, while FiMI Instruct outperforms the Mistral Small 24B Instruct model by 87% on domain-specific tool-calling. Moreover, FiMI achieves these significant domain gains while maintaining comparable performance to models of similar size on general benchmarks.
Unlock Unprecedented Efficiency in Financial Operations
FiMI (Finance Model for India) redefines AI capabilities for the Indian digital payments landscape. Built on the Mistral Small 24B architecture, FiMI offers unparalleled accuracy and domain-specific intelligence, addressing the unique complexities of real-time, multilingual financial workflows.
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Improvement in Finance Reasoning (FiMI Base)
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Higher Accuracy in Tool-Calling (FiMI Instruct)
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Curated Tokens for Pre-training
Deep Analysis & Enterprise Applications
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The Limitations of Generic LLMs in Finance
NPCI's initial attempts to build agentic systems using general-purpose language models faced significant challenges. Despite reasonable tool-calling capabilities, these models struggled with domain-specific terminology like RRN (Retrieval Reference Number) and UMN (Unique Mandate Number), leading to inconsistent and unpredictable results. Relying solely on prompt engineering proved fragile and difficult to scale, highlighting the critical need for a domain-aligned model that internalizes industry-specific terminology, workflows, and constraints for operational robustness.
Strategic Selection: Why Mistral Small 24B?
The choice of foundation model was crucial for accuracy, reliability, and scalability. After benchmarking various candidates against NPCI's specific criteria—including strong general reasoning, robust Indic-language performance (Hindi, Hinglish), low hallucination, predictable tool-calling, and efficient fine-tuning—Mistral Small 24B emerged as the optimal choice. It demonstrated superior agentic behavior, multilingual robustness, and adaptability, all while maintaining manageable inference costs, making it ideal for India-scale financial applications.
| Model | Parameters | MMLU | IFEval | NPCI-Specific Suitability | Licensing & Notes |
|---|---|---|---|---|---|
| Mistral Small 24B | 24B | 79 | 82.9 |
|
Apache 2.0 |
| Llama 3 8B | 8B | 69.4 | 80.4 |
|
Llama 3.1 Community License |
| Mistral Nemo 12B | 12B | 68.0 | 62.9 |
|
Apache 2.0 |
| Mistral 7B | 7B | 60 | 60 |
|
Apache 2.0 |
FiMI's Comprehensive Multi-Phase Training Approach
FiMI's development leverages a sophisticated multi-stage training paradigm to seamlessly adapt the Mistral Small 24B foundation model to the Indian financial ecosystem. This approach progressively internalizes specialized knowledge and operational intelligence, moving beyond simple instruction following to achieve true domain-aligned performance.
Enterprise Process Flow
Continuous Pre-Training (CPT)
→
Instruction Fine-Tuning (IFT)
→
Domain-Specific Supervised Fine-Tuning (SFT)
Crafting Precision: Synthetic Data for UPI Workflows
The UPI Help use case demanded high-quality, structured, multi-turn support workflows with precise tool invocation, which generic datasets lacked. NPCI developed a rigorous synthetic data generation pipeline, incorporating insights from social media queries and domain expert knowledge. This ensured that FiMI internalizes UPI-specific transaction semantics, operational constraints, and regulatory compliance, enabling realistic and robust AI-driven solutions for crucial support scenarios without exposing PII.
Case Study: Enhancing UPI Help with FiMI's Specialized Data
NPCI's UPI Help system required an AI agent capable of real-time transaction checks, error descriptions, and tool-driven actions. Traditional methods failed due to the sensitive nature of financial data and the need for rule-bound operational flows. FiMI's approach involved generating 108,000 high-quality synthetic conversations, validated by SMEs and covering transaction disputes, mandate management, and general financial queries.
This meticulous data engineering enabled FiMI to master complex tool-calling and adhere to strict compliance, transforming UPI Help into a highly reliable and context-aware assistant. The model now supports natural language conversations in English, Hindi, and Hinglish, managing tasks from transaction issue explanations to mandate lifecycle management with unparalleled accuracy.
Quantifiable Performance Gains & Real-World Efficacy
Evaluations reveal FiMI's significant domain specialization while maintaining strong general reasoning. FiMI Base achieved a 20% improvement on finance reasoning benchmarks, while FiMI Instruct recorded an 87% increase in domain-specific tool-calling accuracy over general-purpose models. The multi-phase training, including continuous pre-training on 68 Billion tokens and targeted supervised fine-tuning, successfully aligns the model with complex Indian financial workflows and multilingual user interactions.
20%
Improvement in Finance Reasoning (FiMI Base)
87%
Higher Accuracy in Domain-Specific Tool-Calling (FiMI Instruct)
| Tool | Language | Mistral Small 24 Instruct (F1 Score) | FiMI Instruct (F1 Score) |
|---|---|---|---|
| get_transaction_details | English | 88.89 | 93.47 |
| get_transaction_details | Hindi | 85.62 | 92.58 |
| mandate_summary | English | 83.84 | 94.74 |
| mandate_summary | Hindi | 67.92 | 93.40 |
| mandate_fetch | English | 33.56 | 76.24 |
| mandate_fetch | Hindi | 7.21 | 75.53 |
| mandate_pause | English | 15.38 | 41.98 |
| mandate_pause | Hindi | 4.08 | 40.54 |
| mandate_revoke | English | 33.34 | 77.42 |
| mandate_revoke | Hindi | 14.63 | 76.67 |
| mandate_unpause | English | 35.29 | 68.18 |
| mandate_unpause | Hindi | 0.00 | 51.43 |
Calculate Your Potential ROI with FiMI
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Estimated Annual Savings
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Annual Hours Reclaimed
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Your Journey to AI-Powered Financial Excellence
Our structured implementation roadmap ensures a seamless transition and maximum impact for your organization.
Phase 1: Discovery & Strategy Alignment
Collaborative workshops to define specific use cases, integrate with existing systems, and establish KPIs for domain-specific AI deployment. This phase includes initial data audits and infrastructure assessment.
Phase 2: Custom Model Adaptation & Data Integration
Fine-tuning FiMI with your proprietary financial data, ensuring deep contextual understanding. Development of specialized tools and APIs for seamless integration into your operational workflows.
Phase 3: Pilot Deployment & Optimization
Staged rollout in a controlled environment, rigorous testing, and iterative feedback loops to refine performance. Focus on user adoption, compliance checks, and initial ROI validation.
Phase 4: Full-Scale Rollout & Continuous Improvement
Broader deployment across your enterprise, ongoing monitoring, performance analytics, and regular model updates to adapt to evolving market and regulatory landscapes.
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