In India, personal loan disbursal can happen in minutes. Once the borrower clears the required checks, the money is credited. Business loans, on the other hand, follow a different timeline, often taking days to weeks because the information needed to assess a business is harder to verify and piece together. The lag in the system is hurting small and medium enterprises, and its impact trickles down to the country’s economy. Data shows MSMEs contribute 31.1 per cent of GDP in the country and yet only 41% of the country’s 8.7 crore registered MSMEs have accessed formal credit. At Global Fintech Fest in September, the Prime Minister urged the industry to grow the share of credit alongside payments, making access to finance a central ask for the sector.
Delayed credit can mean a missed order or a late seasonal stock-up, where timing decides whether the business fills the order or loses it. Every extra day spent waiting on a loan decision is a day of lost shelf space, lost inventory, and lost orders.
Looking at the other side of the story
For lenders, business loans carry both an opportunity to extend credit and risks that need to be assessed carefully. Lending money to the wrong borrower can put the lender’s loan book at risk, making thorough verification non-negotiable. Before a rupee moves, a lender needs to ensure four things: that the business is genuine, that it can support the proposed borrowing, that the file meets every regulatory requirement and that all of this happens within a timeframe that works for the business.
However, getting all these checkpoints right starts with one thing: having reliable information that lenders can trust before making a decision. Personal loans sped up when the inputs became trustworthy and machine-readable, with sources such as bureau files, Aadhaar and Account Aggregator systems helping make information easier to access and verify. Business loan files, on the other hand, rarely arrive in a form a system can read: bank statements in different formats, GST filings, scanned deeds and the promoter’s own account of the business. Some of what matters, like the shop and its stock, has to be seen in person. And with larger ticket sizes, every check carries more weight.
Business lending involves multiple stages, each requiring the lender to collect, verify and interpret information. In our experience, more than half of business-loan applications reach lenders through direct selling agents (DSAs), which adds a layer of document hand-offs before the file reaches a credit manager. Following this, the credit manager has to assess business finances and check the borrower’s creditworthiness. They also verify the business and its promoters against relevant legal and public records. The manager will also engage directly or virtually with the borrower to verify details about the business and clarify any questions that emerge during the assessment. The very “trust” factor that is slowing down the process is where automation can make a difference.
How automation can be made possible in business lending
The opportunity for AI agents is to automate more of the work that comes before that judgment. That starts with making the information itself more decision-ready. An agent can collect financial documents, extract relevant information, identify missing pages or discrepancies and link findings back to the underlying records. Instead of sending a credit manager back and forth across multiple files, it can bring the relevant information together in a format that is easier to review.
The same approach can extend to personal discussions. An AI agent can assist with a video call, ask follow-up questions based on risks identified in the financial assessment and prepare a structured, time-stamped record of the conversation. An underwriter’s note that takes hours to prepare manually can be generated in minutes, leaving the credit manager to review the findings rather than build the note from scratch.
Background due diligence is another part of the process that can benefit from this approach. Agents can help gather information from relevant corporate, legal and public records, identify potential issues and consolidate the findings into a report for the credit team. The aim is not to replace verification or judgment, but to reduce the manual effort involved in assembling the evidence.
When these steps work together, lenders can spend less time collecting and reconciling information and more time assessing what it means. That is where the speed gains become meaningful.
How can organisations trust AI?
Given how much is riding on getting this right, financial institutions have every reason to ask hard questions about whether an AI agent deserves the trust it’s asking for. Where that trust actually gets earned is in which vendor a business chooses to work with, and what that vendor’s models were actually built and tested on.
The first thing worth checking is whether the AI was trained on India’s own conditions. India isn’t just another market to plug a global model into; it has its own document formats, languages, business structures, and data patterns, and a model trained mainly on data from elsewhere carries that gap into every file it touches here. Training on Indian data is what makes a model that understands the needs of the market better.
The second is explainability. AI systems can get things wrong or drift over time, which is exactly why every recommendation needs an audit trail behind it. When an agent analyses a file, summarises a call, or recommends a decision, a credit or compliance team needs to trace that recommendation back to its source and see exactly how it was reached. This is especially important for business lending where hefty amounts and business continuity are at stake.
The third is where the decision itself sits. Automating the work leading up to a decision doesn’t mean removing the person who makes it. Agents compile documents, work through thousands of credit parameters, and produce a summary and a recommendation. The credit manager remains the one ending the loop, reviewing that recommendation and making the final call to approve or decline. That keeps every credit decision under human judgment, with AI doing the work that takes care of the trust part.
Speed and scrutiny don’t always have to compete
Lenders were right to insist on real verification, real diligence and a human making the final call. But caution was never supposed to mean slow access. What lenders didn’t have, until recently, was a way to deliver both, real scrutiny and real speed, without one coming at the cost of the other.
AI agents are changing that. By automating the work of gathering information, checking records and preparing evidence, they can help lenders move through the underwriting process faster without removing the scrutiny that responsible lending requires. The opportunity is to make business lending more responsive to the way businesses actually operate, while keeping the final decision where it belongs: with the people accountable for it.
Authored by Vignesh Krishnakumar, Co-founder and CTO at HyperVerge