How ML Models Make Loan Underwriting Process Faster & Fairer
In India, applying for a loan used to mean tons of paperwork, loads of time wasted going from branch to branch, and a lengthy wait for approval. But times are changing. Now, machine learning is optimizing every step in the loan underwriting process, from initial document verification to final selection. Lenders are now assessing the potential risk of lending to a borrower by using data beyond a credit score. Take the case of a loan application for a business that needs some extra working capital. Traditionally, this process took several weeks to go back and forth to a bank branch, physically filing tons of papers. And a final decision that would be predominantly based on whether the business owner had a banking credit history. Today, the same application can be assessed in hours using digital sources of GST filings & UPI transaction patterns. This article examines the workings of loan underwriting process and the technology & data in use. Additionally, diving into the significance of machine learning underwriting for lenders and borrowers in India.
What Is ML Underwriting and How Is It Different From Traditional Credit Scoring?
So, ML underwriting incorporates advanced technology beyond the manual review of credit history scorecards. What is it if not a direct response to everything traditional underwriting could never measure?
Well, it assesses the borrower’s ability to repay the loan by analysing the borrower’s financial data. The loan underwriting process has advanced significantly in the span of five years. Whereas, credit underwriting today depends on how well underwriting models can read patterns lenders once had no way to measure.
The conventional loan underwriting process relied on CIBIL scores, bank statements, and similar documents. It works reasonably well for borrowers with a clean credit history. But it leaves out a large chunk of the population, particularly small business owners and gig workers who simply don't have a long credit trail on record.
ML models look at data, such as repayment history, tax filings, cash flow patterns, and transaction behavior. It helps to get a better picture of a borrower's financial health.
The Alternative Data Powering ML Underwriting: GST, UPI, Account Aggregator, and Beyond
India has created a system that allows lenders to get data from borrowers. This system is called the Account Aggregator framework. It lets borrowers share their bank statements, tax records, and other financial data with lenders. The tax department has allowed lenders to get tax records from borrowers. This means that business owners can share their tax filings with lenders quickly.
When payment patterns are added to this data, lenders can get an idea of a small business's daily earnings. This is a clear example of alternative data for underwriting in practice that shows why machine learning for underwriting models keeps getting more accurate as more of this data becomes available.
In November 2022, the RBI brought GSTN into the Account Aggregator framework to support cash-flow-based MSME lending.
What ML Models Actually Change for Lenders and Borrowers
For lenders, ML underwriting makes the loan process faster and cheaper. A model can evaluate a loan application in seconds and identify potential risks early. This is essentially an automated loan approval process with loan approvals and lower default rates.
For borrowers, this means applying for a loan on their phone and getting an answer the same day. They can get access to money faster. Get loan products that are tailored to their needs. Because a model is only as good as the data it uses and the assumptions it makes.
Automated loan approval systems are steadily moving toward real-time credit underwriting, where a lender can assess risk the moment a transaction happens rather than days later. Industry case material says AI-powered underwriting can cut approval time by up to 50%.
Can ML Models Be Fair?
A common concern is whether this kind of automated assessment can be fair. Getting this right is important for building trust with regulators and borrowers. Algorithms can inherit biases from the data they are trained on. To address this, regulators have introduced guidelines for AI use.
These guidelines are based on principles like trust, fairness, and accountability. It is also important to explain how ML models make decisions. When a rules-based system rejects a loan, the reason is usually clear. ML models can be complex and difficult to understand. Regulators want assurance that credit underwriting stays explainable even as underwriting models grow more advanced.
RBI’s FREE-AI committee framework is built around 7 Sutras, 6 pillars, and 26 recommendations for responsible AI in finance.
Where ML Underwriting Is Heading
Lenders are now using cash flow-based lending to finance businesses. The central bank is encouraging lenders to use tax return data to assess borrowers. Currently, the digital ecosystem for credit decisions is expanding, giving lenders access to more accurate financial information. This shift is what makes automated loan approval possible at a scale that would have been unmanageable under manual review. And it is pushing more lenders toward real-time credit underwriting as the new baseline.
In the future, ML models will be integrated into payment systems and supply chain platforms. This means that businesses can get financing at the moment they make a purchase or transaction. It’s an example of real-time credit underwriting. The significant change, however, is not just about technology. As regulators introduce guidelines for AI use, institutions that have good documentation and processes will be able to make quicker credit decisions.
Conclusion
Loan underwriting is no longer about credit scores. The entire loan process is being rebuilt around this new way of reading risk. More lenders are using machine learning to evaluate borrowers, which means decisions and a broader view of creditworthiness.
Finbase helps financial institutions make this transition with confidence. Its modular design fits into existing systems without disrupting them, and its workflow automation and integrations simplify day-to-day operations. This allows lenders to modernize their underwriting process without sacrificing speed, compliance, or reliability.
FAQs
1. What is ML underwriting?
ML underwriting uses machine learning models to judge a borrower's repayment ability. It uses data like GST filings, UPI transactions and cash flow patterns. This is relying only on a credit score. It helps lenders understand a borrower's behavior better. This is especially true for borrowers who do not have a credit history.
2. What is the difference between ML underwriting and underwriting?
The traditional way of underwriting relies heavily on CIBIL scores and the history of the applicants’ banking. This can exclude small business owners and gig workers. ML underwriting looks at types of data. This helps to judge the creditworthiness rightly and often faster.
3. Can automated loan approval actually speed up lending?
Yes it can. Automated loan approval can evaluate an application in seconds. This is of weeks. Models process data digitally. This is not like document checks. This is why approval times have dropped for lenders.
4. What role does Finbase play in ML underwriting?
Finbase helps lending institutions use ML underwriting. They do not have to change their existing systems. Finbases design is modular. It fits data sources and automation into a lenders current workflow. This makes the shift to credit underwriting smooth. It does not disrupt operations.
5. Will real-time credit underwriting become standard for lending in India?
It seems to be heading that way. As more transaction and payment data becomes available real-time credit underwriting is becoming an option. This is for use cases, like point-of-sale lending. Finbases infrastructure is built to support this kind of shift. This will happen as adoption grows.