Slow, Fragmented Lending Decisions
A regional commercial bank with $6.8 billion in total lending assets and 14 branches was losing competitive ground in commercial and SME lending. Loan officers relied on siloed credit bureau pulls, manually verified financial statements, and subjective risk assessments. The average underwriting cycle stretched to 9 business days — while digital-first competitors were approving similar loans in under 48 hours.
The bank's non-performing loan (NPL) ratio had crept to 4.7% — well above the industry benchmark of 2.5% — directly eroding net interest margins and triggering increased regulatory provisioning requirements. Management recognized that their credit decisioning framework needed a fundamental overhaul.
Core Roadblocks:
- 9-Day Underwriting Cycle: Each commercial loan application required manual data gathering from 5+ sources — credit bureaus, tax filings, bank statements, collateral appraisals, and industry reports — with no automated data aggregation or pre-scoring.
- 4.7% NPL Ratio: The legacy credit scoring model relied on a narrow set of 12 financial ratios, missing behavioral signals, cash flow volatility patterns, and sector-specific risk factors that modern ML models capture.
- Inconsistent Risk Decisions: With 38 loan officers applying subjective judgment to credit memos, approval rates varied by 22% across branches for identical borrower profiles — creating regulatory risk and portfolio concentration issues.
The myAiLabs Ecosystem
myAiLabs deployed its full suite of AI Agents to replace the bank's fragmented, manual credit decisioning with an intelligent, end-to-end underwriting engine. Each agent addressed a critical gap in the lending value chain — from automated data ingestion to real-time portfolio monitoring.
Intelligent Underwriting Engine
The AI-powered underwriting engine fundamentally transformed the bank's credit decisioning. Every loan application now flows through an automated pipeline: financial data is ingested from GST filings, bank statements, and credit bureaus within minutes. The ML scoring model evaluates 47 risk variables — including cash flow volatility, digital transaction patterns, sector cyclicality, and management quality indicators — generating a probability-of-default score with 93% accuracy. Loan officers now receive pre-populated credit memos with AI-recommended terms, pricing suggestions, and flagged risk factors, reducing their role from manual data compilation to informed decision-making. The result: underwriting turnaround dropped from 9 days to under 36 hours, and the NPL ratio fell from 4.7% to 2.2% within 14 months.
Metrics That Matter
The myAiLabs Agentic ecosystem delivered transformative results across credit quality, operational efficiency, and revenue growth within 14 months of deployment.
Lower Defaults
NPL ratio reduced from 4.7% to 2.2% — a 54% improvement driven by the 47-variable ML scoring model.
Faster Decisions
Underwriting turnaround reduced from 9 business days to under 36 hours with automated data aggregation and pre-scoring.
Bad Debt Saved
Annual bad debt reduction through early identification of high-risk borrowers and proactive portfolio monitoring.