finee 1.0.2

Extract structured financial entities from Indian banking messages

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language:

• en license: mit library_name: transformers tags:
• finance
• entity-extraction
• ner
• phi-3
• production
• gguf
• indian-banking
• structured-output base_model: microsoft/Phi-3-mini-4k-instruct pipeline_tag: text-generation

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Finance Entity Extractor (FinEE) v1.0

Extract structured financial data from Indian banking messages in one command.
94.5% field accuracy across HDFC, ICICI, SBI, Axis, Kotak.

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⚡ One-Command Installation

pip install finee

That's it. No cloning, no setup.

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🚀 30-Second Quick Start

from finee import extract

# Parse any Indian bank message
result = extract("Rs.2500 debited from A/c XX3545 to swiggy@ybl on 28-12-2025")

print(result.amount)      # 2500.0
print(result.merchant)    # "Swiggy"
print(result.category)    # "food"
print(result.confidence)  # Confidence.HIGH

Try it live:

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📋 Output Schema Contract

Every extraction returns a guaranteed JSON structure:

{
  "amount": 2500.0,           // float - Always numeric, never "Rs. 2,500"
  "currency": "INR",          // string - ISO 4217 code
  "type": "debit",            // string - "debit" | "credit"
  "account": "3545",          // string - Last 4 digits only
  "date": "28-12-2025",       // string - DD-MM-YYYY format
  "reference": "534567891234",// string - UPI/NEFT reference
  "merchant": "Swiggy",       // string - Normalized name (not "VPA-SWIGGY-BLR")
  "category": "food",         // string - Enum: food|shopping|transport|bills|...
  "vpa": "swiggy@ybl",        // string - Raw VPA
  "confidence": 0.95,         // float - 0.0 to 1.0
  "confidence_level": "HIGH"  // string - "LOW" | "MEDIUM" | "HIGH"
}

Type Definitions (TypeScript-style)

interface ExtractionResult {
  amount: number | null;
  currency: "INR";
  type: "debit" | "credit" | null;
  account: string | null;
  date: string | null;        // DD-MM-YYYY
  reference: string | null;
  merchant: string | null;
  category: Category | null;
  vpa: string | null;
  confidence: number;         // 0.0 - 1.0
  confidence_level: "LOW" | "MEDIUM" | "HIGH";
}

type Category = 
  | "food" | "shopping" | "transport" | "bills"
  | "entertainment" | "travel" | "grocery" | "fuel"
  | "healthcare" | "education" | "investment" | "transfer" | "other";

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🏦 Supported Banks

Bank	Debit	Credit	UPI	NEFT/IMPS
HDFC	✅	✅	✅	✅
ICICI	✅	✅	✅	✅
SBI	✅	✅	✅	✅
Axis	✅	✅	✅	✅
Kotak	✅	✅	✅	✅

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📊 Benchmark

Metric	Value
Field Accuracy	94.5%
Latency (Regex mode)	<1ms
Latency (LLM mode)	~50ms
Throughput	50,000+ msg/sec

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🔧 Installation Options

# Core (Regex + Rules only, no ML)
pip install finee

# With Apple Silicon backend
pip install "finee[metal]"

# With NVIDIA GPU backend
pip install "finee[cuda]"

# With CPU backend (llama.cpp)
pip install "finee[cpu]"

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💻 CLI Usage

# Extract from text
finee extract "Rs.500 debited from A/c 1234"

# Check available backends
finee backends

# Show version
finee --version

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🏗️ Architecture

Input Text
    │
    ▼
┌─────────────────────────────────────────────────────────────┐
│ TIER 0: Hash Cache (<1ms if seen before)                    │
└─────────────────────────────────────────────────────────────┘
    │
    ▼
┌─────────────────────────────────────────────────────────────┐
│ TIER 1: Regex Engine                                        │
│ Extract: amount, date, reference, account, vpa, type        │
└─────────────────────────────────────────────────────────────┘
    │
    ▼
┌─────────────────────────────────────────────────────────────┐
│ TIER 2: Rule-Based Mapping                                  │
│ Map: vpa → merchant, merchant → category                    │
└─────────────────────────────────────────────────────────────┘
    │
    ▼
┌─────────────────────────────────────────────────────────────┐
│ TIER 3: LLM (Optional, for missing fields)                  │
│ Targeted prompts for: merchant, category only               │
└─────────────────────────────────────────────────────────────┘
    │
    ▼
┌─────────────────────────────────────────────────────────────┐
│ TIER 4: Validation + Normalization                          │
│ JSON repair, date normalization, confidence scoring         │
└─────────────────────────────────────────────────────────────┘
    │
    ▼
ExtractionResult (Guaranteed Schema)

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🤝 Contributing

git clone https://github.com/Ranjitbehera0034/Finance-Entity-Extractor.git
cd Finance-Entity-Extractor
pip install -e ".[dev]"
pytest tests/

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📄 License

MIT License - see LICENSE

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Made with ❤️ by Ranjit Behera

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