Hugging Face's logo

Datasets:
AgamiAI
/
Indian-Bank-Statements

Tasks:
Text Classification
Table Question Answering
Text Generation
Modalities:
Document
Languages:
English
Hindi
Size:
10K<n<100K
Tags:
finance
Synthetic
banking
india
transactions
bank-statements
License:
Dataset card Data Studio Files
xet
Community
Indian-Bank-Statements / README.md
sandeepkachru's picture
sandeepkachru
Update README.md
e921370 verified
preview code
|
raw
history blame contribute delete
7.44 kB
metadata
language:
  - en
  - hi
license: apache-2.0
size_categories:
  - 10K<n<100K
task_categories:
  - text-classification
  - table-question-answering
  - text-generation
tags:
  - finance
  - synthetic
  - banking
  - india
  - transactions
  - bank-statements
  - document-ai
pretty_name: Indian Bank Statement Synthetic Dataset

Indian Bank Statement Synthetic Dataset

Synthetically generated Indian business bank statements with realistic transaction patterns, proper banking workflows, and India-specific features. Available in scanned PDF and digital JSON formats.

Scope: Current Accounts (business banking) only. Does not include personal/savings accounts.

Dataset Details

Note: Contains only legitimate transactions (no fraud patterns).

Uses

Suitable For

  • Document AI and OCR training
  • Information extraction (account numbers, balances, transactions)
  • Transaction categorization and classification
  • Financial document understanding
  • Table extraction and parsing
  • Named Entity Recognition (NER)
  • Testing data processing pipelines
  • Educational purposes

Not Suitable For

  • Fraud detection or AML (no fraudulent patterns)
  • Production compliance or regulatory reporting
  • Credit decisions (lacks real creditworthiness signals)
  • Personal banking AI (business accounts only)

Dataset Structure

Statement Formats

Type 1: Separate Debit/Credit Columns

Date Description Debit Credit Balance
01/01/2024 UPI-Vendor 450.00 - 25,780.50
02/01/2024 NEFT Credit - 50,000.00 75,780.50

Type 2: Single Transaction Column

Date Description Transaction Balance
01/01/2024 UPI-Vendor -450.00 25,780.50
02/01/2024 NEFT Credit +50,000.00 75,780.50

JSON Structure 

{
  "bank_name": "Paramount Banking Corporation",
  "account_holder": "CYIENT TECHNOLOGIES",
  "account_holder_address": "F-346\nThird Floor\nHinjewadi\nPune\nMaharashtra\n520018",
  "account_number": "90823789756",
  "ifsc_code": "PARA0761987",
  "micr_code": "899946557",
  "branch_name": "PUNE HINJEWADI",
  "branch_code": "6738",
  "account_type": "CURRENT ACCOUNT- GENERAL",
  "currency": "INR",
  "customer_id": "134743833",
  "opening_balance": 158458.03,
  "closing_balance": 64424.49,
  "start_date": "2024-01-01",
  "end_date": "2024-03-31",
  "statement_date": "2025-11-20",
  "interest_rate": 2.83,
  "transactions": [
    {
      "date": "2024-01-01 12:40:40",
      "value_date": "2024-01-01",
      "description": "NEFT Dr-471179370408-HDFC0009038-RIDDHI RAVAL",
      "cheque_no": "862512",
      "debit": 13932.79,
      "credit": null,
      "balance": 144525.24,
      "branch_code": "3421",
      "failed": false
    }
  ]
}

Transaction Types

  • UPI: Unified Payments Interface (DR/CR)
  • NEFT: National Electronic Funds Transfer
  • RTGS: Real Time Gross Settlement (high-value)
  • IMPS: Immediate Payment Service, salary transfers
  • Cheques: Chq Paid, By Clg (Clearing)
  • Cash: Withdrawals and deposits
  • ATM: ATM withdrawals
  • Service Charges: Bank fees
  • Reversals: Failed transaction reversals

Dataset Creation

Why This Dataset

India's digital payment ecosystem is rapidly growing, but publicly available datasets for training AI models on Indian business banking documents are scarce due to privacy constraints. This dataset provides production-quality synthetic data for:

  • Training document AI on Indian bank statement formats
  • Testing OCR and information extraction systems
  • Building fintech applications without real customer data
  • Both scanned (unstructured) and digital (structured) formats
  • India-specific payment systems (UPI, IMPS, NEFT, RTGS)

Data Generation

Fully synthetic - no real customer information:

  • Probabilistic modeling of realistic business transaction patterns
  • Proper debit/credit flows with accurate balance calculations
  • India-specific features: UPI references, IFSC/MICR codes, Indian business names
  • Business entities: IT companies, manufacturing, retail, financial services
  • Geographic coverage: Mumbai, Delhi, Bangalore, Pune, Chennai, Kolkata, Hyderabad
  • Both scanned PDFs and structured JSON

All data is algorithmically generated. No real individuals or businesses contributed data.

What's Included

  • Account holders: Business entities (companies, partnerships, corporations)
  • Transaction patterns: B2B payments, employee salaries, vendor payments, business expenses
  • Regional diversity: Major Indian metros
  • Temporal patterns: Quarterly statements, monthly salary cycles, vendor payment patterns

Limitations

  1. No fraud patterns - Not suitable for fraud detection
  2. Business-only - No personal/savings account patterns
  3. Urban business focus - May not represent rural small businesses
  4. Simplified patterns - Real-world complexity is higher
  5. Format coverage - Common layouts only, not exhaustive
  6. Synthetic OCR - May not include all real-world OCR challenges

This dataset is for structure and format learning, not behavioral modeling. Always validate on real data before production deployment.

Citation

BibTeX:

@dataset{indian_bank_statement_synthetic_2025,
  author = {AgamiAI Inc.},
  title = {Indian Bank Statement Synthetic Dataset},
  year = {2025},
  publisher = {HuggingFace},
  url = {https://huggingface.co/datasets/AgamiAI/Indian-Bank-Statements}
}

APA:

AgamiAI Inc. (2025). Indian Bank Statement Synthetic Dataset [Data set]. HuggingFace. https://huggingface.co/datasets/AgamiAI/Indian-Bank-Statements

Glossary

Indian Banking Terms:

  • UPI: Unified Payments Interface - instant real-time payment system
  • NEFT: National Electronic Funds Transfer - batch processing (half-hourly)
  • RTGS: Real Time Gross Settlement - high-value transactions (₹2 lakh+)
  • IMPS: Immediate Payment Service - instant transfer, 24/7
  • IFSC Code: Indian Financial System Code - 11-character bank branch identifier
  • MICR Code: Magnetic Ink Character Recognition - 9-digit code for cheque processing
  • Current Account: Business/commercial account, no transaction limits

More Information

About AgamiAI

AgamiAI builds private AI solutions for enterprises where privacy, accuracy, and compliance are critical. Specialized in Finance, Healthcare, Legal, and Consulting.

Visit: https://www.agami.ai

File Structure

Each statement includes:

  • [statement_id].pdf - Scanned bank statement
  • [statement_id].json - Structured data with full metadata

Related Datasets

Part of AgamiAI's Indian Financial Documents collection:

  • Indian Bank Statements (this dataset)
  • Indian GST Documents (coming soon)
  • Indian Tax Documents (coming soon)
  • Indian Audited Financial Documents (coming soon)

Contact

Version: 1.0.0 | License: Apache 2.0 | Last Updated: November 2025

Privacy Notice: Entirely synthetic data. No real personal or financial information included.