Expense Receipt OCR and AI-Driven Insight Generation for Sales CRM Systems

Expense management in modern organizations is increasingly dependent on digital workflows and automation systems. However, many organizations still rely on manual expense receipt processing and fragmented operational analysis methods. Traditional OCR systems frequently fail when handling low-quality receipts, payment screenshots, inconsistent layouts, and noisy image conditions. Additionally, organizations require intelligent business insights derived from CRM operational data for effective decision-making. This paper presents a hybrid Expense Receipt OCR and AI- Driven Insight Generation framework integrated into a Sales CRM platform. The proposed system combines image preprocessing, hybrid OCR extraction using Tesseract.js and Python OCR ensemble methods, machine learning based field extraction, expense category prediction, confidence-aware review logic, and AI-powered CRM insight generation. The framework supports both physical receipts and digital payment screenshots from applications such as Google Pay, PhonePe, and Paytm. The OCR module extracts structured expense information including vendor name, date, GST amount, category, total amount, and confidence metrics. The AI insight module analyzes CRM operational data including leads, deals, quotations, meetings, expenses, and follow-ups to generate business recommendations using Gemini AI models with deterministic fallback support. The proposed architecture improves operational efficiency, reduces manual expense processing effort, enhances structured data extraction, and supports intelligent business decision-making in CRM environments.