AgamiAI/Indian-Bank-Statements

1---2language:3- en4- hi5license: apache-2.06size_categories:7- 10K<n<100K8task_categories:9- text-classification10- table-question-answering11- text-generation12tags:13- finance14- synthetic15- banking16- india17- transactions18- bank-statements19- document-ai20pretty_name: Indian Bank Statement Synthetic Dataset21---22 23# Indian Bank Statement Synthetic Dataset24 25Synthetically generated Indian **business bank statements** with realistic transaction patterns, proper banking workflows, and India-specific features. Available in **scanned PDF** and **digital JSON** formats.26 27**Scope:** Current Accounts (business banking) only. Does not include personal/savings accounts.28 29## Dataset Details30 31- **Curated by:** AgamiAI Inc.32- **Language(s):** English, Hindi (romanized)33- **License:** Apache 2.034- **Repository:** https://huggingface.co/datasets/AgamiAI/Indian-Bank-Statements35- **Website:** https://www.agami.ai36 37**Note:** Contains only legitimate transactions (no fraud patterns).38 39## Uses40 41### Suitable For42- Document AI and OCR training43- Information extraction (account numbers, balances, transactions)44- Transaction categorization and classification45- Financial document understanding46- Table extraction and parsing47- Named Entity Recognition (NER)48- Testing data processing pipelines49- Educational purposes50 51### Not Suitable For52- Fraud detection or AML (no fraudulent patterns)53- Production compliance or regulatory reporting54- Credit decisions (lacks real creditworthiness signals)55- Personal banking AI (business accounts only)56 57## Dataset Structure58 59### Statement Formats60 61**Type 1: Separate Debit/Credit Columns**62| Date | Description | Debit | Credit | Balance |63|------|-------------|-------|--------|---------|64| 01/01/2024 | UPI-Vendor | 450.00 | - | 25,780.50 |65| 02/01/2024 | NEFT Credit | - | 50,000.00 | 75,780.50 |66 67**Type 2: Single Transaction Column**68| Date | Description | Transaction | Balance |69|------|-------------|-------------|---------|70| 01/01/2024 | UPI-Vendor | -450.00 | 25,780.50 |71| 02/01/2024 | NEFT Credit | +50,000.00 | 75,780.50 |72 73### JSON Structure74 75```json76{77  "bank_name": "Paramount Banking Corporation",78  "account_holder": "CYIENT TECHNOLOGIES",79  "account_holder_address": "F-346\nThird Floor\nHinjewadi\nPune\nMaharashtra\n520018",80  "account_number": "90823789756",81  "ifsc_code": "PARA0761987",82  "micr_code": "899946557",83  "branch_name": "PUNE HINJEWADI",84  "branch_code": "6738",85  "account_type": "CURRENT ACCOUNT- GENERAL",86  "currency": "INR",87  "customer_id": "134743833",88  "opening_balance": 158458.03,89  "closing_balance": 64424.49,90  "start_date": "2024-01-01",91  "end_date": "2024-03-31",92  "statement_date": "2025-11-20",93  "interest_rate": 2.83,94  "transactions": [95    {96      "date": "2024-01-01 12:40:40",97      "value_date": "2024-01-01",98      "description": "NEFT Dr-471179370408-HDFC0009038-RIDDHI RAVAL",99      "cheque_no": "862512",100      "debit": 13932.79,101      "credit": null,102      "balance": 144525.24,103      "branch_code": "3421",104      "failed": false105    }106  ]107}108```109 110### Transaction Types111 112- **UPI**: Unified Payments Interface (DR/CR)113- **NEFT**: National Electronic Funds Transfer114- **RTGS**: Real Time Gross Settlement (high-value)115- **IMPS**: Immediate Payment Service, salary transfers116- **Cheques**: Chq Paid, By Clg (Clearing)117- **Cash**: Withdrawals and deposits118- **ATM**: ATM withdrawals119- **Service Charges**: Bank fees120- **Reversals**: Failed transaction reversals121 122## Dataset Creation123 124### Why This Dataset125 126India'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:127 128- Training document AI on Indian bank statement formats129- Testing OCR and information extraction systems130- Building fintech applications without real customer data131- Both scanned (unstructured) and digital (structured) formats132- India-specific payment systems (UPI, IMPS, NEFT, RTGS)133 134### Data Generation135 136**Fully synthetic** - no real customer information:137- Probabilistic modeling of realistic business transaction patterns138- Proper debit/credit flows with accurate balance calculations139- India-specific features: UPI references, IFSC/MICR codes, Indian business names140- Business entities: IT companies, manufacturing, retail, financial services141- Geographic coverage: Mumbai, Delhi, Bangalore, Pune, Chennai, Kolkata, Hyderabad142- Both scanned PDFs and structured JSON143 144All data is algorithmically generated. No real individuals or businesses contributed data.145 146### What's Included147 148- **Account holders:** Business entities (companies, partnerships, corporations)149- **Transaction patterns:** B2B payments, employee salaries, vendor payments, business expenses150- **Regional diversity:** Major Indian metros151- **Temporal patterns:** Quarterly statements, monthly salary cycles, vendor payment patterns152 153## Limitations154 1551. **No fraud patterns** - Not suitable for fraud detection1562. **Business-only** - No personal/savings account patterns1573. **Urban business focus** - May not represent rural small businesses1584. **Simplified patterns** - Real-world complexity is higher1595. **Format coverage** - Common layouts only, not exhaustive1606. **Synthetic OCR** - May not include all real-world OCR challenges161 162This dataset is for structure and format learning, not behavioral modeling. Always validate on real data before production deployment.163 164## Citation165 166**BibTeX:**167 168```bibtex169@dataset{indian_bank_statement_synthetic_2025,170  author = {AgamiAI Inc.},171  title = {Indian Bank Statement Synthetic Dataset},172  year = {2025},173  publisher = {HuggingFace},174  url = {https://huggingface.co/datasets/AgamiAI/Indian-Bank-Statements}175}176```177 178**APA:**179 180AgamiAI Inc. (2025). *Indian Bank Statement Synthetic Dataset* [Data set]. HuggingFace. https://huggingface.co/datasets/AgamiAI/Indian-Bank-Statements181 182## Glossary183 184**Indian Banking Terms:**185- **UPI**: Unified Payments Interface - instant real-time payment system186- **NEFT**: National Electronic Funds Transfer - batch processing (half-hourly)187- **RTGS**: Real Time Gross Settlement - high-value transactions (₹2 lakh+)188- **IMPS**: Immediate Payment Service - instant transfer, 24/7189- **IFSC Code**: Indian Financial System Code - 11-character bank branch identifier190- **MICR Code**: Magnetic Ink Character Recognition - 9-digit code for cheque processing191- **Current Account**: Business/commercial account, no transaction limits192 193## More Information194 195### About AgamiAI196 197AgamiAI builds private AI solutions for enterprises where privacy, accuracy, and compliance are critical. Specialized in Finance, Healthcare, Legal, and Consulting.198 199Visit: **https://www.agami.ai**200 201### File Structure202 203Each statement includes:204- `[statement_id].pdf` - Scanned bank statement205- `[statement_id].json` - Structured data with full metadata206 207### Related Datasets208 209Part of AgamiAI's Indian Financial Documents collection:210- **Indian Bank Statements** (this dataset)211- Indian GST Documents (coming soon)212- Indian Tax Documents (coming soon)213- Indian Audited Financial Documents (coming soon)214 215### Contact216 217- **Website**: https://www.agami.ai218- **HuggingFace**: https://huggingface.co/AgamiAI219 220---221 222**Version:** 1.0.0 | **License:** Apache 2.0 | **Last Updated:** November 2025223 224**Privacy Notice:** Entirely synthetic data. No real personal or financial information included.