Let me provide a detailed breakdown of our implementation across all three integration points:
Mobile App Integration:
We built a custom expense submission screen in Pega Mobile Client using the native camera integration. When employees click ‘Submit Expense’, the mobile app captures the receipt image and immediately uploads it to a Pega case as an attachment. The mobile app uses offline capability so employees can snap receipts even without connectivity, and submissions sync automatically when back online.
The key integration point is the mobile app’s event handler that triggers the RPA workflow. After the image uploads, the mobile app calls a Pega REST service endpoint that creates an expense case and queues it for RPA processing. We pass metadata from the mobile app (employee ID, expense category selected by user, submission timestamp) as case properties. The mobile app then displays a confirmation screen showing the case ID and estimated processing time. Employees can track status in real-time through the mobile app, which polls the case status every 30 seconds and updates the UI.
OCR-Based Receipt Validation:
The RPA bot picks up queued expense cases and downloads the receipt image attachment. It calls Google Cloud Vision API for OCR extraction, which returns structured data including merchant name, date, total amount, line items, and payment method. The bot has built-in validation logic that checks OCR confidence scores - if confidence is below 85%, the case is automatically routed to manual review rather than proceeding with automated validation.
For high-confidence OCR results, the bot performs multiple validation checks: date validation (expense must be within 90 days), amount validation (must match receipt total), merchant validation (cross-checks against approved vendor list), and duplicate detection (compares against recent submissions to catch duplicate claims). If the receipt is in a foreign currency, the bot calls an exchange rate service API to convert to USD using the date from the receipt for historical rate accuracy.
The bot also performs image quality checks using computer vision - it flags receipts that are too dark, too blurry, or have significant portions cut off. These quality issues trigger a notification back to the employee’s mobile app asking them to resubmit a clearer photo. This feedback loop significantly improved our OCR accuracy from the initial 70% to current 92%.
Automated Approval Workflow:
After validation, the bot evaluates policy compliance using Pega decision tables. We have tiered approval rules: expenses under $50 with valid receipts are auto-approved immediately, $50-$500 require manager approval only if they fail policy checks (out-of-policy merchant, weekend expense, etc.), and over $500 always route to manager approval regardless of policy compliance.
The decision tables check against 15 different policy rules including per-diem limits, category-specific caps (meals, lodging, transportation), blacklisted merchants, and timing rules. When all policy checks pass, the bot automatically approves the expense, updates the case status, triggers reimbursement processing in our financial system via API, and sends a push notification to the employee’s mobile app confirming approval and expected reimbursement date.
For expenses that require human approval, the bot prepares a summary package including the OCR-extracted data, policy compliance analysis, and receipt image, then routes to the appropriate manager’s work queue. The manager sees a pre-filled approval screen with all relevant data already populated, reducing their review time from 5-10 minutes per expense down to 30 seconds for a simple approve/reject decision.
Implementation took 8 weeks with a team of 3 developers. ROI was achieved in 4 months based on time savings for both employees and managers. Our auto-approval rate is currently 78%, with the remaining 22% routed for human review due to policy exceptions or OCR confidence issues. Employee satisfaction scores for the expense process improved from 4.2 to 8.7 out of 10 after deployment.