Accelerating SME Lending: Automated Credit Decision Engine for a Regional Fintech

A US-based Fintech lender focused on providing working capital loans to Small & Medium Businesses (SMEs) had a loan approval process that took 5–7 days, causing them to lose customers to faster, larger banks. The bottleneck was manual underwriting: analysts had to manually download bank statements, verify business entity status, and calculate debt-service coverage ratios (DSCR) using Excel. Major bottlenecks: • 5-7 day loan approval timeline • Manual underwriting process with Excel spreadsheets • Losing customers to faster competitors • High operational costs per loan origination • Limited scalability without hiring more staff

We developed an "Automated Underwriting Workbench" that reduced the "File-to-Decision" time from days to minutes: • Open Banking Integration: Integrated banking APIs to fetch 12 months of applicant bank transaction data instantly, removing the need for customers to upload PDFs • Fraud Detection Layer: Built a pattern-recognition engine to detect "synthetic IDs" and circular transactions (wash trading) to flag high-risk applications automatically • Auto-Spreading Parser: Developed a tool that reads tax returns and bank statements, automatically extracting key financial ratios (EBITDA, Cash Flow) and populating the risk model • Decision Matrix: Implemented a rule-based engine that auto-approves low-risk loans and routes only "gray area" applications to human underwriters

Outstanding business impact delivered: • Speed: Time-to-Decision reduced from 5 days to 4 hours • Cost Efficiency: Operational cost of origination per loan dropped by 65% • Scalability: Successfully processed 3x the loan volume in Q4 without hiring additional underwriters • Customer Experience: Dramatically improved with near-instant decisions • Risk Management: Enhanced fraud detection and automated risk assessment • Competitive Advantage: Now competing effectively with larger banks on speed