How Lenders Evaluate Borrowers: Key Factors In Credit Underwriting

Lenders don’t just approve or reject a loan application based on a single factor. Instead, they take a holistic view of a borrower’s financial profile to determine whether granting credit is a safe and viable decision. From checking credit scores to assessing spending behaviour, modern underwriting is a blend of traditional and tech-driven risk evaluation.

Here’s a closer look at the factors that influence credit underwriting decisions in India.

1. Credit Score and Repayment Behaviour

The credit score is one of the first things a lender examines when assessing a borrower’s creditworthiness. In India, credit scores are issued by major credit bureaus such as CIBIL, Equifax, Experian, and CRIF High Mark, based on a borrower’s financial history.

A higher credit score (typically 750 and above) indicates responsible credit usage and timely repayments, leading to:

• Faster loan approvals
• Lower interest rates
• Higher loan amounts

However, a poor credit score (below 650) can result in:

• Loan rejections
• Higher interest rates
• Stricter repayment terms

Beyond the score itself, lenders also analyse a borrower’s repayment behaviour. Consistently missed EMIs, frequent delays, or past defaults raise red flags, making it harder to secure new loans.

2. Income Stability and Source of Earnings

Lenders assess whether a borrower has a stable source of income to ensure consistent repayment ability. This factor is particularly crucial for unsecured loans (such as personal loans) where there is no collateral backing the loan.

What lenders check:

• For salaried individuals: Employer reputation, job tenure, and monthly salary. Those working in government jobs or well-established private firms often get loans more easily.
• For self-employed individuals: Business stability, annual turnover, profit margins, and financial records such as tax returns and GST filings.
• For freelancers/gig workers: Some lenders now consider alternative income sources such as contract work, rental income, and even digital earnings.

A steady and predictable income increases the chances of loan approval, whereas irregular earnings or job instability may result in a higher interest rate or outright rejection.

3. Debt-to-Income Ratio (DTI) – How Much Debt Is Too Much?

Even if a borrower has a good income, lenders check how much of it is already committed to existing debt obligations. This is measured using the Debt-to-Income (DTI) ratio, which is calculated as:

DTI= (Total Monthly Debt Payments / Total Monthly Income) ×100

For example, if someone earns ₹1,00,000 per month but already pays ₹50,000 in EMIs, their DTI ratio is 50%.

Why does this matter?

• A DTI below 40% is considered safe, meaning the borrower can manage additional loan repayments.
• A DTI above 50% signals financial strain, making lenders hesitant to approve new credit.

Lenders prefer borrowers with a lower DTI because it reduces the risk of over-leveraging, which could lead to missed payments or defaults.

4. Type of Loan and Security Provided – Secured vs Unsecured Lending

Not all loans are assessed equally. The underwriting process varies depending on whether the loan is secured (backed by collateral) or unsecured (granted purely based on creditworthiness).

• Secured Loans (Home Loans, Auto Loans, Gold Loans, etc.) – Since the lender has an asset as security, credit risk is lower. Even borrowers with moderate credit scores may qualify if the collateral holds sufficient value.
• Unsecured Loans (Personal Loans, Credit Cards, Business Loans, etc.) – These loans are riskier for lenders, leading to stricter credit evaluations and higher interest rates for applicants with weaker financial profiles.

For business loans, lenders also assess company performance, industry risks, and financial stability before making a lending decision.

5. Alternative Data And AI-Based Underwriting

With the rise of digital lending, many lenders now go beyond traditional credit scores and use alternative data to evaluate creditworthiness.

This includes:

1. Utility bill payments – A borrower who consistently pays electricity, mobile, and rent bills on time may be considered financially responsible.
2. Spending habits – Lenders analyse banking transactions to see how much a borrower saves, invests, or spends each month.
3. Digital footprints – Some AI-based models assess online transactions, subscriptions, and even shopping patterns to predict financial behaviour.

For borrowers without a formal credit history (such as young professionals or gig workers), these alternative credit models offer a fairer assessment, allowing them to access loans even if they don’t have a CIBIL score.

6. Compliance with RBI Regulations

Lenders must also ensure that their underwriting process follows the Reserve Bank of India’s (RBI) regulations, which are frequently updated to improve financial stability.

Recent RBI measures include:

• Stricter underwriting for unsecured loans to prevent excessive risk-taking.
• AI and credit risk model guidelines to ensure fair lending decisions.
• Mandatory credit reporting every 15 days to improve borrower transparency.

For P2P lending platforms and fintech lenders, RBI has imposed additional checks to protect borrowers from predatory lending practices and ensure transparency in loan disbursals.

How Borrowers Can Improve Their Creditworthiness

Understanding these factors can help borrowers improve their chances of securing a loan with favourable terms. Some simple yet effective steps to improve credit score include:

• Maintaining a high credit score by paying EMIs and credit card bills on time.
• Keeping the Debt-to-Income ratio below 40% to ensure financial stability.
• Demonstrating income stability, whether through steady employment or consistent business earnings.
• Building a credit history by using small credit products like secured credit cards or buy-now-pay-later (BNPL) services responsibly.

How Technology Is Changing Credit Underwriting In India

The way lenders assess borrowers has changed dramatically over the past decade. What was once a slow, manual process dependent on paperwork and human judgment is now faster, data-driven, and automated. Thanks to advancements in AI, alternative data, and automation, credit underwriting is becoming more efficient, accurate, and accessible.

Let’s break down the biggest changes.

AI Is Replacing Manual Credit Assessment

Traditionally, loan approvals involved human underwriters reviewing salary slips, bank statements, and credit history. This process was time-consuming and often biased towards borrowers with well-documented incomes.

Today, AI-driven underwriting models can:

• Assess credit risk instantly by analysing thousands of data points.
• Detect fraud by identifying document inconsistencies.
• Predict repayment behaviour using advanced algorithms.

Lenders no longer rely only on credit scores—they now use AI models to predict future financial behaviour based on transaction history, spending patterns, and even digital payments.

Borrowers Without A Credit Score Can Now Get Loans

One of the biggest problems in India’s lending ecosystem has always been the lack of formal credit histories. Millions of people—especially gig workers, small business owners, and young professionals—struggle to get loans because they don’t have a CIBIL score.

To solve this, many lenders are now using alternative credit scoring models, which take into account:

• Utility bill payments (electricity, mobile, rent)
• Spending and saving patterns from bank accounts
• Digital payment transactions (UPI, wallets, BNPL services)

This approach has made credit more inclusive, allowing first-time borrowers to access loans without relying on traditional credit reports.

Loans Are Getting Approved Faster With Automated Underwriting

In the past, loan approvals could take days or even weeks because banks had to manually verify documents and assess risk. Today, many lenders have moved to automated underwriting systems, where AI handles the entire decision-making process.

Here’s how automated underwriting works:

• Borrowers apply online, and their financial data is instantly retrieved.
• AI analyses income, spending behaviour, and creditworthiness.
• Loan approval (or rejection) happens within minutes, with minimal human involvement.

For personal loans, credit cards, and small-ticket financing, many fintech lenders now offer real-time approvals, making borrowing easier and faster.

Video KYC And Digital Onboarding Have Replaced Paperwork

With RBI pushing for digital banking, loan applications no longer require physical paperwork. Instead, lenders now use:

• Aadhaar-based e-KYC for instant identity verification.
• Video KYC to complete onboarding remotely.
• AI-driven document verification to detect fraud and forged details.

These changes have reduced operational costs for lenders and made borrowing seamless for customers, particularly in rural and semi-urban areas.

Blockchain Could Make Credit Histories More Transparent

Although still in the early stages, blockchain technology has the potential to make credit underwriting more secure and tamper-proof. If widely adopted, it could:

• Store borrower credit histories on a decentralised network, preventing fraud.
• Allow borrowers to own and share their financial data securely with lenders.
• Reduce dependency on centralised credit bureaus and speed up loan approvals.

While blockchain-based lending hasn’t become mainstream yet, it’s expected to play a larger role in the future of trust-based digital credit models.

Technology is reshaping the lending landscape, and borrowers need to understand how it impacts them.

If you’re applying for a loan, this means:

• Faster loan approvals (often within minutes).
• More accurate risk assessments, reducing unfair rejections.
• Better access to credit, even for those without a CIBIL score.

Challenges And Limitations In Credit Underwriting

While credit underwriting has become faster and more data-driven, it is far from perfect. Lenders still face challenges in accurately assessing risk, ensuring fair loan approvals, and preventing fraud. On the other hand, borrowers often struggle with inconsistent lending criteria, outdated credit models, and transparency issues.

Let’s explore some of the biggest challenges and limitations that affect credit underwriting in India today.

1. Incomplete Credit Histories Still Impact Borrowers

Despite technological advancements, millions of Indians still struggle to get loans due to a lack of formal credit history. This issue is most common among:

• First-time borrowers (students, young professionals).
• Gig workers and freelancers with irregular incomes.
• Small business owners who do not have well-documented financials.

Even though alternative credit scoring methods (such as analysing utility bill payments and digital transactions) are gaining traction, most banks and NBFCs still rely heavily on traditional credit scores. This means many deserving borrowers get rejected simply because they don’t fit into conventional risk models.

2. Over-reliance On Credit Scores Can Be Misleading

A high credit score does not always mean a borrower is financially responsible, and a low score does not always mean they are risky. Traditional credit scoring models have limitations, such as:

• Not accounting for sudden financial improvements (e.g., a borrower may have struggled in the past but is now earning well).
• Failing to consider alternate income sources (many people earn from side businesses, investments, or freelance work that doesn’t reflect in official income records).
• Overlooking contextual factors (a missed EMI due to an emergency should not be weighed the same as habitual defaults).

This rigid scoring system often leads to unfair loan rejections, particularly for self-employed individuals and informal sector workers.

3. Inconsistent Lending Policies Across Lenders

There is no standard underwriting model followed across the lending industry. Each bank, NBFC, and fintech lender has its risk assessment framework, leading to inconsistencies in loan approvals.

For example:

• One lender might approve a loan for a borrower with a 680 credit score, while another might reject them outright.
• Some banks have strict income criteria, whereas digital lenders consider transaction behaviour instead.
• Loan terms (interest rates, tenure, and fees) can vary widely for the same borrower based on the lender’s internal policies.

This lack of uniformity makes it difficult for borrowers to understand what they qualify for and why they were rejected.

4. Rising Loan Fraud And Identity Theft

With more lenders shifting to digital underwriting, fraudsters are finding new ways to manipulate the system. Some common fraud risks include:

• Fake financial documents – Fraudulent salary slips, fake bank statements, and forged tax returns.
• Identity theft – Using stolen Aadhaar and PAN details to apply for loans.
• Loan stacking – Borrowers taking multiple loans from different lenders simultaneously before their credit reports update.

Although AI and data analytics help detect fraud patterns, many lenders still rely on traditional verification methods, making them vulnerable to sophisticated fraud schemes.

5. Bias In AI-Based Underwriting Models

AI and machine learning have made credit underwriting faster and more efficient, but they also come with risks—especially bias in decision-making.

• AI models are trained on historical lending data, which means if past lending decisions were biased (e.g., rejecting self-employed borrowers more often), the AI might continue reinforcing those biases.
• Some AI-driven underwriting systems lack transparency, making it difficult for borrowers to challenge loan rejections.
• Borrowers from lower-income groups or rural areas may be unfairly categorised as high-risk, simply because they don’t have enough digital financial data.

Without proper regulation, AI-based lending can become just as unfair as traditional underwriting, if not worse.

6. Regulatory Uncertainty And Changing RBI Guidelines

The Reserve Bank of India (RBI) frequently updates lending regulations to prevent excessive risk-taking and consumer exploitation. While these changes are necessary, they create challenges for lenders who must constantly adapt their underwriting models.

Some recent regulatory shifts that have impacted underwriting include:

• Stricter personal loan guidelines to prevent over-lending.
• Mandatory fortnightly credit reporting to reduce risk from multiple loans.
• Tighter regulations for digital lenders and BNPL (Buy Now, Pay Later) providers to protect borrowers.

While these changes improve financial stability, they also make it harder for lenders to create a consistent underwriting framework, especially fintech startups that rely on digital credit models.

Conclusion

Credit underwriting in India has evolved significantly, shifting from manual paperwork-based approvals to AI-driven, data-driven decision-making. Today, lenders use a mix of traditional credit scores, alternative data sources, and AI-based risk models to assess borrowers. While these advancements have made loan approvals faster and more accessible, challenges such as credit exclusions, fraud risks, and regulatory uncertainty still persist.

For borrowers, understanding how underwriting works can help improve creditworthiness and increase loan approval chances. Meanwhile, for lenders, embracing transparency, standardised risk models, and fair lending practices will be key to ensuring a sustainable lending ecosystem.