When Rajiv Mehta, a shop owner in Gujarat, applied for a working capital loan from a traditional bank, he was prepared for paperwork. What he didn’t expect was the long wait, repeated queries about his cash-based earnings, and the final rejection citing reasons such as insufficient credit history. His struggle is not unique. For many years, the process of assessing the creditworthiness of small and medium-sized businesses has been slow, opaque, and often exclusionary. All this is fast changing, with the inclusion of new age technologies such as artificial intelligence (AI), machine learning (ML), automation, and analytics among others. These technologies are revolutionising credit underwriting, rendering it faster, smarter, more inclusive, and increasingly digital. In this blog, we explore this evolution and how technology is changing the face of credit underwriting.
Understanding Credit Underwriting
At its core, credit underwriting is about evaluating risk. When a business applies for a loan, lenders must determine whether they can repay it. Traditionally, this involved combing through balance sheets, profit-and-loss statements, tax filings, and other documentation. With large institutions, this process is extremely time consuming. With smaller businesses that may not have the formal financial records, loans were not even considered.
In India, credit underwriting for businesses typically involves five stages:
- Lead capture – includes collecting applications via agents or digital platforms
- Document verification through Aadhaar, PAN, and GST data
- Credit assessment which includes analysing credit scores, cash flows, and financials
- Compliance checks like AML, KYC, and fraud screening
- Final decision-making on granting or rejecting the loan
Traditionally, in the lending world, underwriting was considered more art than science. Lenders often relied on the judgment of credit officers, who assessed applicants using experience and instinct. This led to biased decisions, unpredictable outcomes, slow processing, and widespread inefficiencies. Today, technology has become the turning point in this process, enabling lenders to make safer, quicker, and better decisions.
The Turning Point from Paper to Digital and Technology
The COVID-19 pandemic shook up global financial markets in a way we had not seen since the 2008 financial crisis. Banks and financial lending organisations realised the value of risk assessment and the technology associated with it. This was an opportunity to upend the slow and outdated processes. The pandemic further accelerated digitisation.
Artificial Intelligence (AI) has been the star of the show - transforming credit underwriting by automating processes, offering broader data sources, thereby making way for smarter risk evaluation. The benefits - quicker decisions, reduced fraud, and greater access to credit for underserved businesses. Research indicates that digital lending platforms have boosted loan approvals by up to 45% while cutting defaults by 25%[1].
Let’s take a closer look at how AI and other new age technologies have impacted every step of the credit underwriting process:
#1: Lead Management and Digital Onboarding
Businesses typically approach lenders directly or through agents, submitting loan applications with supporting documents such as financial statements, bank account summaries, tax records, and identity proofs. Web portals and mobile apps allow businesses to apply for these loans online, also allowing banks and lending organisations accelerate the data gathering process. Platforms are available that easily ingest application data and documents through Optical Character Recognition (OCR) technology, automated uploads or API integrations. Predictive diallers, CRM integration, and automated lead categorisation tools reduce the burden on sales teams.
#2: Document Verification and Data Extraction
Lenders must validate submitted documents to ensure authenticity and accuracy. This includes verifying KYC (Know Your Customer) records, proof of income, and compliance with regulatory norms. Biometric technology is used to establish the identity of the borrower. OCR technology converts scanned documents into editable and searchable data. AI-enhanced OCR tools extract relevant information (e.g., GST numbers, income figures) with high accuracy, eliminating the need for manual data entry.
#3: Credit Risk Assessment, Due Diligence, and Compliance Checks
This involves checking credit scores (like CIBIL in India or Experian globally), analysing financial statements, evaluating debt-service coverage ratios, and understanding business cash flows and market conditions. Banks and lending organisations can perform AML (Anti-Money Laundering) checks, fraud risk assessments, and ensure that the applicant is not listed under any sanctions.
Through integrations with Aadhaar, PAN databases, and credit bureaus, customer identity and credit scores can be verified in real time. Regulatory APIs allow instant checks for AML and blacklists, ensuring compliance without delays. AI and ML are transforming how lenders evaluate creditworthiness. Instead of relying solely on financial statements, lenders now tap into alternative data: utility bill payments, mobile phone usage, transaction patterns, and even social media activity.
#4: Credit Decision and Loan Sanction
Based on all inputs, underwriters decide to approve, reject, or seek more information. For approved loans, the amount and interest terms are finalised. Credit platforms often incorporate configurable rule engines. For example, if a borrower meets all predefined criteria, the loan can be auto-approved without underwriter intervention. These straight-through processing (STP) cases speed up disbursement.
#5: Disbursement and Loan Monitoring
Funds are disbursed, and the borrower’s performance is tracked regularly to identify early signs of stress. Underwriters and credit heads can monitor the health of loan portfolios using dashboards that aggregate data from all touchpoints. Heatmaps, delinquency trackers, and risk alerts support better oversight and faster reaction to emerging risks.
Transforming Underwriting in India: Challenges and Emerging Opportunities
While technology has clearly made credit underwriting simpler, faster, and more efficient, there are still several opportunities waiting to be explored. For instance, India’s small business ecosystem is vast but fragmented. There are 5.93 crore registered Micro, Small, and Medium Enterprises (MSMEs) in India, employing over 25 crore people[2]. Despite their critical role in the economy, only a fraction of MSMEs have access to formal credit. By 2024, just 20% of micro and small enterprises had secured loans from scheduled banks, and only 9% of medium-sized businesses had done the same. This is especially striking given that MSMEs are driving India’s rise as a manufacturing powerhouse, contributing nearly 46% of the country’s exports[3]. Traditional underwriting often fails to capture the financial realities of these businesses, with many depending on cash transactions or are lacking in formally audited statements.
Technology is stepping in to bridge this gap. Initiatives like India Stack, a set of APIs for identity verification, UPI for payments, and the Account Aggregator framework - are enabling secure data-sharing between banks and other lenders like fintech companies. Businesses can now authorise access to their financial data through a consent-based system, giving lenders a fuller picture of their economic activity. Companies are going a step forward and incorporating generative AI into their credit risk processes. It’s still at an exploratory stage, as McKinsey’s survey among a small group (24 financial institutions) revealed that only 20% had one use case of gen AI in their organisations[4].
The regulatory framework in India is also evolving with the rapid changes and requirements in the lending industry. RBI’s Digital Lending Guidelines (2022) have further boosted transparency and customer protection, requiring digital lenders to disclose all charges and ensure data privacy[5]. The Digital Personal Data Protection Act (2023), much like the GDPR, focuses on user consent, data minimisation, and limitations on purpose[6].
Smarter Together: The Future of Human–AI Teamwork
Credit underwriting has travelled a long way, from gut feeling to data-driven science. For business owners like Rajiv Mehta, the digital shift offers more than just speed. It promises access, fairness, and the chance to grow without bureaucratic friction. However, even the most advanced algorithms are not likely to fully replace human judgment. Edge cases, fraud flags, and ethical considerations still require a human touch. The best systems combine automation with oversight, coupled with human insight and experience. For example, flagging high-risk loans for manual review. Lenders need to ensure that algorithms are explainable and transparent. The rise of ‘responsible AI’ is encouraging, but still a work in progress. In the future, underwriting will become even more predictive and personalised. Technology isn't just improving underwriting — it’s democratising it.
[1] https://yourstory.com/2025/03/beyond-credit-scores-digital-solutions-transforming-india-credit-underwriting
[2] https://pib.gov.in/PressReleasePage.aspx?PRID=2099687
[3] https://pib.gov.in/PressReleasePage.aspx?PRID=2099687
[4] https://www.mckinsey.com/capabilities/risk-and-resilience/our-insights/embracing-generative-ai-in-credit-risk
[5] https://www.rbi.org.in/commonman/english/scripts/FAQs.aspx?Id=3413
[6] https://prsindia.org/billtrack/digital-personal-data-protection-bill-202
- Category: Business Process Services
- Date: 15-05-2025