Transformed Loan Underwriting to support hypergrowth
Automated 70% for the Loan underwriting process,
and reduced risk by improving the approval process.
Process
The client is a leading Indian bank which was looking at expanding their 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 requirement. 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 difficult to address by simply adding manpower, as it resulted in deteriorating of the portfolio results and high delinquency rates.
Solution
AI/ML based solution was developed 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. Additional integration was done with the bank’s Core Banking system and loan management system to seamlessly capture all available information about the SME. External system integration with government and third-party data sources was done to capture all external information that would be needed for underwriting. Post which an Artificial Intelligence (AI) based Underwriting Decision Logic system was built 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 finally approve or reject the loan.
Challenges Addressed
High Turnaround Time (TAT)
High TAT results in challenges in growing the portfolio. Customer satisfaction is affected adversely by the high TAT.
Low standardization
The process was more based on experience of underwriters. This resulted in individual bias creeping in decisions and lack of consistency in portfolio.
Lack of automation and loosely integrated systems to assess SMEs
Most banks do not have their Current Account data integrated with the lending platform which caused lack of detailed insight for underwriters.
Manually intensive underwriting process
The process was low on automation and required manual intervention in most stages. Hence, growth in business was depended on hiring more resources.
Highly analytical in nature
SME lending requires high level of analytical skills for underwriters and training new resources or keeping them updated to changes becomes a challenge.
Outcome
- Reduced TAT for underwriting loans by 60%.
- Loan volume increase by 200% without increasing the underwriting staff.
- 12% increase in portfolio profitability.