Transform Loan Underwriting and Reduce Risk to Support Hypergrowth
100
%
Reduction in TAT for underwriting loans.
100
%
Increase in loan volume without increasing underwriting staff.
0
%
Increase in portfolio profitability.
The Process
The client is a leading Indian bank that was looking at expanding its Small to Medium Enterprise (SME) loan portfolio. India currently has close to 9 million registered Micro, Small, and Medium Enterprises (MSME), most of which are using the parallel economy to meet short term and long term funding requirements. This segment is hence a high potential area for growth for banks and lending institutions.
However, meeting the requirement of increasing the SME loan portfolio was challenging to address by merely adding human resources, as it resulted in deteriorating of the portfolio results and high delinquency rates.
The Solution
We developed a solution using Aiwozo’s AI/ML activities for the bank to automate approximately 70% of the SME loan underwriting process. The first step was to digitize the SME loan application to remove manual intervention in data capture. Aiwozo performed additional integration with the bank’s Core Banking system and loan management system to capture all available information about the SME seamlessly.
External system integration with government and third-party data sources was done to capture all external data that would be needed for underwriting. Post which we built an Artificial Intelligence (AI) based Underwriting Decision Logic system to capture the analytical steps performed by underwriters. Once the machine-based decision is derived, the entire data is then passed to senior underwriters to approve or reject the loan finally.
Challenges Addressed
Key business and operational challenges that limited performance, scalability, and governance before automation was introduced.
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High TAT -
Loosely integrated systems -
Manual process
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Low standardization -
Highly analytical in nature