Scaling SME Loan Underwriting for Hypergrowth Without Scaling Risk

100 %
Reduction in TAT for underwriting loans.
100 %
Increase in loan volume, same underwriting staff.
0 %
Increase in portfolio profitability.
The Process

The client is a leading Indian bank that set out to expand its Small and Medium Enterprise (SME) loan portfolio. India is home to close to 9 million registered Micro, Small, and Medium Enterprises (MSME), most of which rely on informal credit to fund their operations, making this segment a significant growth opportunity for banks that can underwrite it effectively.

However, the bank found that scaling this portfolio through manual effort alone was not sustainable. Underwriting was manual, judgment-heavy, and slow, and adding more underwriters did not resolve the issue; it only introduced greater inconsistency in decision-making and rising delinquency rates.

  • High turnaround time on loan decisions
  • Manual data capture from paper based applications
  • Core banking and loan management systems operating in isolation from one another
  • Underwriting quality varying significantly by analyst, with no standardized approach
  • A highly analytical process constrained by limited human bandwidth
The Solution

Aiwozo deployed autonomous agents to manage the entire underwriting workflow spanning data capture, external verification, and decision analysis while ensuring that final loan approval remained with senior underwriters.

Capture agents digitized SME loan applications at the point of intake, eliminating the need for manual data entry.

Integration agents connected directly with the bank’s Core Banking and Loan Management systems, as well as government and third-party data sources, to automatically compile a complete financial profile of each SME applicant.

Underwriting Decision agents replicated the analytical judgment a human underwriter would apply, consistently assessing each applicant against defined risk criteria and compiling a decision ready case for review.

Senior underwriters then reviewed each agent generated recommendation to approve or reject the loan, preserving accountability at the final decision point while removing the manual effort required to reach it.

Challenges Addressed

Key business and operational challenges that limited performance, scalability, and governance before automation was introduced.

  • High TAT
  • Loosely integrated systems
  • Manual process
  • Low standardization
  • Highly analytical in nature
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