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Case Study
AI-Powered Receipt & Payment Document Recognition System with Fraud Detection
  • Industry:
    Fintech / Digital Banking / Payments
    1
  • Project Type:
    Computer Vision & AI-Powered Document Processing
    2
  • Duration:
    ~5 months
    3
  • Team Size:

    7+ specialists (PM, BA, CTO, ML Engineers, Backend Developers, DevOps, QA)
    4
  • Technology Stack:

    Python, OpenCV, Keras, TensorFlow, YOLO, CNNs, LLMs, Docker, CI/CD
    5
  • Customer
    NDA
    6
Executive Summary
A leading fintech company processing thousands of customer support tickets daily faced critical challenges with manual verification of payment receipts, invoices, and bank transfer confirmations. The manual review process was time-consuming, error-prone, and vulnerable to fraud attempts using manipulated or fake documents.
Our solution: We developed an advanced AI-powered document recognition and fraud detection system that automatically processes receipts in multiple formats (PDF, JPEG, PNG), supports multiple languages (Russian, Ukrainian, English), and identifies fraudulent documents with high accuracy.


Key Results:
  • 95%+ accuracy in automated document recognition
  • 80% reduction in manual verification time
  • Near-zero fraud penetration rate with multi-layered detection
  • Processing time: Under 5 seconds per document
  • Multilingual support: 5+ languages with complex scripts
  • Scalable architecture: Plugin-based system for easy expansion
Client & Context
The client operates a digital payment platform processing customer support requests via JIRA ticketing system. Each ticket often includes:
  • Payment receipts from various banks and payment systems
  • Transfer confirmations (Ukrainian, Russian, international banks)
  • Screenshots of mobile banking apps
  • PDF invoices and payment instructions
Business Pain Points:
  • Manual Processing Bottleneck: Support agents spent 5-10 minutes per document verification
  • High Error Rate: Human fatigue led to 15-20% error rate in peak hours
  • Fraud Vulnerability: Sophisticated scammers used edited PDFs and images
  • Language Barriers: Documents in Russian, Ukrainian, English with different fonts and layouts
  • Scalability Issues: Inability to handle traffic spikes during peak periods
  • Compliance Risk: Insufficient audit trail for regulatory requirements
Why They Chose Us:
  • Proven expertise in fintech document processing
  • Deep understanding of Eastern European payment systems
  • Advanced fraud detection capabilities using AI/ML
  • Rapid development cycle with plug-and-play architecture
  • Experience with JIRA integration and enterprise systems
Technical Challenge
Problem Complexity
Document recognition in fintech presents unique challenges far beyond simple OCR:
  • Document Quality Variability
    • Low-resolution screenshots from mobile devices
    • Skewed images from poor camera angles (rotation up to 45°)
    • Noise interference (background patterns, watermarks, anti-fraud overlays)
    • Poor lighting conditions
    • Compression artifacts from multiple image uploads
  • Format Diversity
    • PDF documents: Text-based and scanned images
    • Images: JPEG, PNG with varying quality
    • Types: Bank receipts, payment instructions, mobile banking screenshots, wire transfer confirmations
  • Multilingual & Multi-Script Support
    • Cyrillic (Russian, Ukrainian, Belarusian)
    • Latin scripts with special characters
    • Mixed language documents (English headers, Cyrillic content)
    • Varying fonts, sizes, and styles (bold, italic, handwritten annotations)
  •  Layout Variations
    • Each bank/payment system has unique templates
    • Same bank may have multiple versions over time
    • Mobile vs. web-generated receipts differ significantly
    • No standardization across payment providers
  • Fraud Detection Requirements
    • Detect PDF text manipulation (font inconsistencies, position anomalies)
    • Identify image editing (cloned areas, unnatural shadows)
    • Recognize screenshot editing tools artifacts
    • Flag template-based fake receipts
    • Verify logical consistency (amounts, dates, timestamps)
  • Performance Requirements
    • Real-time processing (under 5 seconds)
    • Zero downtime deployment
    • Scalability to handle 10,000+ documents/day
    • Integration with existing JIRA workflow
    • Asynchronous processing with load balancing
Our Solution
We designed a comprehensive, multi-layered document processing and fraud detection system leveraging cutting-edge AI technologies.
System Architecture
Pipeline Overview
JIRA Ticket → Document Queue → Preprocessing → Layout Analysis → Text Extraction →
Fraud Detection → Decision Engine → Result Storage → Audit Trail
Phase 1: Advanced Image Preprocessing
Before any recognition, documents undergo sophisticated preprocessing to maximize OCR accuracy:
Skew Correction
  • Technology: Hough Transform + Projection Profile Analysis
  • Process: Detects text lines, calculates rotation angle, applies affine transformation
  • Result: Corrects skew angles from -45° to +45° with 0.1° precision
  • Example Output: "Angle corrected: 0.00°"
Noise Removal
  • Technology: Gaussian Blur + Bilateral Filtering
  • Process: Removes background watermarks, security patterns, compression artifacts
  • Preserves: Text edges and critical document features
  • Adaptive: Automatically adjusts filter strength based on noise level
Contrast Enhancement
  • Technology: CLAHE (Contrast Limited Adaptive Histogram Equalization)
  • Process: Enhances local contrast while preventing over-amplification
  • Benefit: Improves text visibility in low-quality scans
Sharpening
  • Technology: Unsharp Masking
  • Process: Enhances text edges for better character recognition
  • Parameters: Dynamically adjusted based on blur estimation
Binarization
  • Technology: Adaptive Thresholding (Otsu's Method + Local Adaptive)
  • Process: Converts grayscale to black-white while handling varying lighting
  • Result: Clean text separation from background
Morphological Operations
  • Technology: Opening, Closing, Dilation, Erosion
  • Process: Removes small artifacts, connects broken characters, fills gaps
  • Purpose: Prepares optimal input for OCR engines
Phase 2: Multi-Strategy Recognition
We implemented three parallel recognition strategies to ensure maximum coverage:
Strategy 1: PDF Text Extraction (99% Accuracy for Native PDFs)
Use Case: Digitally-generated PDF receipts from modern banking systems
Technology Stack:
  • Python libraries: PyPDF2, pdfplumber, PDFMiner
  • Custom post-processing algorithms
Process:
  1. Text Extraction: Extract native text with position, font, and size information
  2. Layout Analysis: Map text elements to document structure
  3. Template Matching: Identify bank/payment system by layout patterns
  4. Fraud Detection:
  • Analyze font consistency (unexpected font changes indicate editing)
  • Check text positioning (irregular spacing suggests manipulation)
  • Validate text rendering (compare with expected bank templates)
  • Detect embedded images in text layers (sign of screenshot insertion)
Example Detection:
  • Original Bank Receipt: Consistent font (Arial 10pt), aligned fields, standard spacing
  • Fraudulent Document: Mixed fonts (Arial + Times), irregular positioning, spacing anomalies
Advantages:
  • Near-perfect text extraction for native PDFs
  • Preserves formatting and structure
  • Fast processing (under 1 second)
  • Highly effective fraud detection through metadata analysis
Strategy 2: General OCR (85-90% Accuracy for Images)
Use Case: Screenshots, scanned documents, mobile photos

Technology Stack:
  • OCR Engine: Google Tesseract 4.x + TesseractOCR with custom training
  • Language Models: Russian, Ukrainian, English + special character sets
  • Confidence Scoring: Per-word and per-character accuracy metrics
Process:
  1. Preprocessing: Apply all 6 preprocessing steps
  2. Language Detection: Auto-identify document language
  3. Multi-language OCR: Parallel processing with multiple language models
  4. Post-processing:
  • Confidence filtering (reject low-confidence results)
  • Spell-checking with financial terminology dictionaries
  • Format validation (dates, amounts, account numbers)
Example Results (Ukrainian Receipt):

Recognized Text

Confidence Score

"monobank"

99.99%

"Квитанція" (Receipt)

99.39%

"Універсал Банк"

81.45%

Account Number

84.36%

Amount: "500"

84.10%


Challenges Addressed:
  • Low confidence for certain fields (e.g., payment system "МС" = 24.37%)
  • Font variations causing recognition issues
  • Background patterns interfering with text detection
Solution: Hybrid approach combining general OCR with specialized bank-specific models
Strategy 3: Bank-Specific Deep Learning Models (95%+ Accuracy)
Use Case: High-volume receipts from specific banks/payment systems

Technology Stack:
  • Framework: Keras + TensorFlow
  • Architecture: Convolutional Neural Networks (CNNs)
  • Training: Custom datasets per bank template
Process:
3.1 Layout Manager (Document Classification)
  • Model: YOLOv4/YOLOv8 for object detection
  • Purpose: Identifies document type and bank/payment system
  • Training: Thousands of samples per template
  • Output: Document classification + region of interest (ROI) coordinates
3.2 Region-Specific OCR
  • Model: Custom CNN trained on specific banks
  • Process:
  • Layout Manager identifies document structure
  • Each region (sender, recipient, amount, date) processed separately
  • Specialized neural networks for each field type
  • Convolution operations on limited character sets for higher accuracy
3.3 Plug & Play Architecture
  • Design: Model Factory Pattern
  • Benefit: Add new bank without retraining entire system
  • Scalability: Each bank plugin is independent and optimized separately
  • Fallback: If no specific plugin matches, general OCR is used
Example Workflow:
Document → Layout Manager detects "MonoBank Template v2" → Load MonoBank Plugin →
Extract Sender (99% accuracy) → Extract Amount (98% accuracy) → Extract Date (99% accuracy) → Validate logical consistency → Return structured JSON


Phase 3: Multi-Layered Fraud Detection
Our fraud detection system operates at multiple levels:
Layer 1: Metadata Analysis (PDF Documents)
  • Font consistency checking
  • Creation timestamp validation
  • Software signature verification
  • Embedded object analysis
Layer 2: Visual Forensics (Images)
  • Clone detection (duplicate regions)
  • Error Level Analysis (ELA) for JPEG manipulation
  • Noise pattern analysis
  • Shadow and lighting consistency
Layer 3: Template Verification
  • Compare against genuine bank templates
  • Validate logos, watermarks, security elements
  • Check for known fraud patterns from database
Layer 4: Logical Validation
  • Cross-field consistency (amount in text vs. numeric)
  • Date/time plausibility checks
  • Account number format validation
  • Transaction ID verification
Layer 5: Machine Learning Anomaly Detection
  • Neural network trained on fraud patterns
  • Behavioral analysis of document characteristics
  • Confidence scoring for final decision
Decision Engine:
  • Green Flag: All checks passed, auto-approve
  • Yellow Flag: Minor inconsistencies, queue for quick human review
  • Red Flag: High fraud probability, block and escalate
Phase 4: LLM Integration for Context Understanding
Latest Enhancement: We integrated Large Language Models (LLMs) for advanced context understanding:
Use Cases:
  • Natural Language Instructions: Understanding payment purpose descriptions in multiple languages
  • Entity Recognition: Extracting beneficiary names, company names, complex address formats
  • Semantic Validation: Checking if payment description matches transaction details
  • Multi-language Translation: Normalizing documents from different languages
Technology:
  • OpenAI GPT-4 API / Anthropic Claude API
  • Custom fine-tuned models for financial domain
  • Prompt engineering for structured data extraction
Example:
Input: "Переказ особистих коштів" (Ukrainian)
LLM Output: 
{
  "payment_purpose": "personal_funds_transfer",
  "language": "uk",
  "confidence": 0.97,
  "translated": "Transfer of personal funds"
}
Implementation Details
Integration & Delivery
JIRA Integration
  • Webhook triggers: Automatic document processing on ticket creation/update
  • Attachment extraction: Download and process all document attachments
  • Status updates: Real-time feedback to support agents via JIRA comments
  • Audit trail: Complete processing history linked to ticket
Asynchronous Processing Architecture
  • Message Queue: RabbitMQ / Redis for load balancing
  • Worker Pools: Horizontal scaling with multiple processing nodes
  • Priority Queue: VIP customers / high-value transactions get faster processing
  • Retry Logic: Automatic retries with exponential backoff
Docker Containerization
Services:
  • - document-processor (Python)
  • - ocr-worker (Tesseract + Custom Models)
  • - fraud-detector (ML Models)
  • - api-gateway (REST API)
  • - database (PostgreSQL + Redis cache)
  • - monitoring (Prometheus + Grafana)
CI/CD Pipeline
  • GitLab CI: Automated testing, building, deployment
  • Testing: Unit tests, integration tests, visual regression tests
  • Deployment: Blue-green deployment for zero downtime
  • Rollback: Instant rollback capability if issues detected
Monitoring & Observability
  • Metrics: Processing time, success rate, fraud detection rate
  • Alerts: Real-time notifications for system anomalies
  • Dashboards: Executive dashboards for business KPIs
  • Logging: Centralized logging with ELK stack
Documentation & Training
Delivered Artifacts:
  1. Technical Documentation:
  • System architecture diagrams
  • API documentation (OpenAPI/Swagger)
  • Database schema and data flows
  • Deployment guides
  1. Model Documentation:
  • Training procedures
  • Dataset specifications
  • Model performance metrics
  • Retraining guidelines
  1. Operational Runbooks:
  • Troubleshooting guides
  • Scaling procedures
  • Incident response playbooks
  • Backup and recovery procedures
  1. User Training:
  • Support agent training materials
  • Admin panel user guides
  • Video tutorials
  • FAQ documentation
Results & Business Impact
Performance Metrics

Metric

Before

After

Improvement

Processing Time

5-10 min/document

<5 sec/document

100x faster

Manual Review Rate

100%

15-20%

80% reduction

Accuracy Rate

82% (human error)

95%+

+13% improvement

Fraud Detection

60% caught

98% caught

+38% improvement

Support Capacity

200 tickets/day

1000+ tickets/day

5x increase

Cost per Document

$2.50

$0.25

90% cost reduction

Business Outcomes
Operational Efficiency:
  • Support team reallocated to complex cases requiring human judgment
  • Peak hour handling capacity increased 5x
  • Customer waiting time reduced from 2 hours to <15 minutes
  • Ticket resolution SLA compliance improved from 75% to 97%
Financial Impact:
  • Annual savings: $850,000 in operational costs
  • Fraud prevention: $1.2M in blocked fraudulent transactions (first year)
  • ROI: 420% in first 18 months
  • Revenue protection: Prevented reputation damage from fraud incidents
Compliance & Risk:
  • Complete audit trail for all document processing
  • Regulatory compliance for KYC/AML requirements
  • Reduced liability exposure from manual verification errors
  • Enhanced data security with encrypted document storage
Customer Satisfaction:
  • Customer complaint rate reduced 45%
  • Net Promoter Score (NPS) increased from 32 to 58
  • Faster dispute resolution
  • Improved trust through transparent automated verification
Technical Achievements
Scalability:
  • Handles 10,000+ documents/day (tested up to 50,000/day)
  • Auto-scaling based on queue depth
  • Distributed processing across multiple regions
  • 99.9% uptime achieved
Flexibility:
  • 15+ bank templates added in first 6 months
  • New payment systems integrated within 1-2 weeks
  • Multilingual expansion from 3 to 5+ languages
  • Continuous improvement through automated retraining
Security:
  • Zero data breaches since deployment
  • PCI DSS compliance maintained
  • SOC 2 Type II certification achieved
  • End-to-end encryption for all document data
Unique Solution Advantages
Innovation Highlights
Plug & Play Scalability
Unlike traditional OCR systems requiring complete retraining, our architecture allows:
  • Add new bank template: 2-3 days for data collection + training
  • No system downtime: Hot-swap model deployment
  • Independent optimization: Each plugin optimized separately
  • Graceful fallback: General OCR used when specific plugin unavailable
Technical Advantage: Model Factory Pattern + Dynamic Model Loading
Multi-Layered Preprocessing Pipeline
Most OCR solutions apply basic preprocessing. We implemented:
  • 6-stage preprocessing vs. industry standard 2-3 stages
  • Adaptive algorithms that adjust to document quality
  • Quality scoring to determine optimal processing path
  • Before/After validation ensuring preprocessing improved quality
  • Result: 15-20% accuracy improvement over standard preprocessing
Hybrid Recognition Strategy
Rather than "one size fits all," we use:
  • PDF-native extraction for digital documents (fastest, most accurate)
  • General OCR for unknown formats (highest coverage)
  • Specialized CNNs for high-volume banks (optimal accuracy)
  • Benefit: Each document processed via optimal method, maximizing overall efficiency
Advanced Fraud Detection
Goes beyond simple OCR validation:
  • Multi-vector analysis: Metadata + visual + logical + ML
  • Continuous learning: Fraud database updated with new patterns
  • Risk scoring: Probabilistic approach vs. binary accept/reject
  • Explainable AI: Clear reasons for fraud flags (compliance requirement)
  • Impact: 98% fraud catch rate with <2% false positives
LLM Context Understanding
Latest innovation integrating large language models:
  • Semantic comprehension of payment purposes
  • Multilingual normalization without manual translation
  • Entity extraction handling complex real-world variations
  • Contextual validation checking logical consistency
  • Example: Understanding "Payment for services rendered per contract #123" in Russian and validating against contract database
Technology Stack Deep Dive
Core TechnologiesComputer Vision & ML
  • OpenCV 4.x: Image preprocessing, morphological operations
  • Tesseract 4.x: General OCR engine with custom training
  • YOLOv4/YOLOv8: Layout detection and document classification
  • TensorFlow 2.x + Keras: Custom CNN models for bank-specific recognition
  • Scikit-learn: Fraud detection ML models
Language & Frameworks
  • Python 3.9+: Core application language
  • FastAPI: High-performance async REST API
  • Celery: Distributed task queue for async processing
  • Pydantic: Data validation and settings management
Infrastructure
  • Docker + Docker Compose: Containerization
  • Kubernetes: Orchestration (production environment)
  • Redis: Caching + message broker
  • PostgreSQL: Primary database
  • MinIO/S3: Document storage
DevOps & Monitoring
  • GitLab CI/CD: Continuous integration and deployment
  • Prometheus: Metrics collection
  • Grafana: Visualization and dashboards
  • ELK Stack: Centralized logging (Elasticsearch, Logstash, Kibana)
  • Sentry: Error tracking and alerting
AI/ML Tools
  • Jupyter Notebooks: Model development and experimentation
  • MLflow: Model versioning and experiment tracking
  • DVC (Data Version Control): Dataset versioning
  • Label Studio: Document annotation for training data
Lessons Learned & Best Practices
Technical Insights
1. Preprocessing is Critical
  • Finding: 60% of accuracy issues resolved by better preprocessing
  • Best Practice: Invest heavily in adaptive preprocessing pipeline
  • Recommendation: Always validate preprocessing improved input quality
2. Language-Specific Challenges
  • Cyrillic Recognition: Required custom training datasets
  • Mixed Scripts: Needed multi-model approach for mixed-language documents
  • Special Characters: Banking symbols (₴, ₽, $) needed special handling
  • Solution: Maintain language-specific model variations + fallback chains
3. Fraud Patterns Evolve
  • Challenge: Fraudsters constantly adapt techniques
  • Approach: Continuous monitoring + retraining cycle
  • Best Practice: Maintain fraud pattern database with versioning
  • Key: Balance false positive rate vs. fraud catch rate
4. Model Performance Monitoring
  • Issue: Production data drift reduced accuracy over time
  • Solution: Automated model performance tracking
  • Trigger: Auto-retrain when accuracy drops below threshold
  • Result: Maintained 95%+ accuracy consistently