Bank Statement Analyzer
Upload any bank statement PDF and receive a structured spending report with category breakdowns, anomaly flags, and cash-flow trends — powered by AI extraction and classification.
Statement Upload
User uploads one or more bank statement PDFs via a secure drag-and-drop interface.
PDF Parsing
Python extraction engine reads and normalises transaction rows regardless of bank layout.
AI Classification
Claude API categorises each transaction and flags anomalies; results are stored in PostgreSQL.
Report Generation
A structured spending report with charts, category totals, anomaly list, and cash-flow trend is generated and delivered.
Project Scope & Capabilities
Multi-Bank PDF Parsing
Layout-agnostic extraction engine that accurately reads transaction data from PDFs exported by any major bank, regardless of formatting differences.
AI Transaction Categorisation
Claude API classifies every transaction by category (payroll, utilities, marketing, etc.), sub-category, and merchant type — with a confidence score for review.
Anomaly Detection
Automated flagging of duplicate charges, unusually large transactions, and new recurring payments outside the client's normal spending pattern.
Implementation Timeline
| Phase | Duration | Description |
|---|---|---|
| Discovery & Sample Collection | Week 1 | Collect sample PDFs from target banks, map transaction field variations, design categorisation taxonomy, and define anomaly rules. |
| Core Development | Weeks 2-3 | Build Python PDF parser, Claude API categorisation pipeline, PostgreSQL schema, anomaly detection logic, and report generation. |
| Testing & Calibration | Week 3-4 | Validate parsing accuracy across 10 bank formats, calibrate category prompts, and test anomaly detection with real statement samples. |
| Deployment & Handover | Week 4 | Production API deployment, client upload interface, monitoring setup, and documentation handover. |
Cost Analysis
Development
$4,800
PDF parsing engine, Claude API classification pipeline, anomaly detection, PostgreSQL schema, and reporting interface.
Infrastructure
$90/mo
FastAPI cloud hosting, PostgreSQL database, Claude API token costs, and secure file storage.
Maintenance
$120/mo
New bank format onboarding, category taxonomy updates, monitoring, and technical support.
Return on Investment
95% faster
Analysis Time
A full month's statement is categorised and reported in under 30 seconds instead of hours of manual review.
96.4%
Categorisation Accuracy
AI classification matches or exceeds the accuracy of a trained bookkeeper across all transaction types.
12 hrs/month
Bookkeeping Hours Saved
Automated analysis eliminates most of the manual transaction review and categorisation workload.
3x more
Anomalies Caught
AI detection surfaces duplicate charges and irregular payments that manual review routinely misses.
Testing & Quality Assurance
Automated Testing
- ✓ PDF parser tested against statements from 10 different banks
- ✓ Categorisation accuracy benchmarked on 2,000 labelled transactions
- ✓ Anomaly detection validated with 40 injected duplicate and outlier charges
- ✓ API load tested with concurrent uploads of 20 simultaneous statements
Manual Validation
- ✓ Accountant review of category assignments across 3 months of real transactions
- ✓ Anomaly report spot-checked against client's own records
- ✓ Report layout and chart clarity reviewed by end users
- ✓ Upload interface accessibility and security tested by the client team
"What used to take our bookkeeper half a day every month now runs automatically overnight. The anomaly detection alone has already caught two duplicate vendor charges we would never have noticed."
Ready to Automate Your Financial Analysis?
Let's discuss how AI-powered bank statement analysis can save your team hours every month and surface insights that manual review misses.