The lending landscape in India has been transformed massively over the last decade. Traditional personal loan approvals took days, if not weeks, requiring a lot of paperwork, income, and credit history verification, as well as multiple visits to bank branches. However, with the rise of artificial intelligence (AI), lenders can now see risk, verify eligibility for a borrower, and approve loans in a matter of minutes. AI-based credit underwriting is changing the way that banks, NBFCs, and fintech platforms lend money to their clients, as the process has become faster than ever before, ensuring a safe and accessible loan.

This well-rounded guide is going to discuss the usage of Artificial Intelligence (AI) in credit underwriting and its benefits, the data it operates on, as well as the legalities of regulations in India and what the future holds for borrowers and lenders alike.

1. What is AI-based Credit Underwriting?

Credit underwriting is a process that loan providers use to check if a borrower is qualified for a loan or not, and on what terms. Traditionally, this included:

• Identity checks and income check
• Checking Credit history (CIBIL or others credit bureaus)
• Looking at debt to income ratio
• Manual evaluation of applications

AI-based credit underwriting eliminates much of this manual process by implementing machine learning algorithms, big data analytics and predictive models. These algorithms consider borrower risk by analyzing enormous amounts of structured and unstructured data such as:

• Bank account transactions
• Payment histories
• Employment patterns
• Social signals (with consent)
• Spending behavior

AI systems can then be used to score borrowers almost instantly, allowing for easier and faster approvals, as well as tailored loan offers.

2. Why Lenders are turning to AI

A. Speed and Efficiency

• AI takes days of processing time into minutes.
• Automated systems can handle evaluations of thousands of applications at a time.

B. Better Risk Assessment

• With the help of predictive analytics, AI models are able to spot the possibility of defaults before they occur.
• This enables the lenders to adjust either the interest rate, the amount of loan or the duration depending upon individual risk profiles.

C. Lower Operational Costs

• Automation reduces the manpower needed to do the manual verification.
• Less paper work means fewer errors and slower processing delays.

D. Financial Inclusion

• AI lets lenders look at non-traditional data sources, which means that it is now possible to provide loans to gig workers, freelancers, or people with little credit history.
• This increases access to credit for people in underserved sectors of the population.

3. How AI is used in Credit Underwriting

AI-based credit underwriting generally consists of a number of stages:

A. Data Collection

AI systems collect information from various sources in the form of:

• Traditional Sources: Credit bureau reports (CIBIL, Experian, Equifax) Bank statements, income proof
• Alternative Sources: UPI transaction history, the use of mobile wallet, paying bills, social media behavior (with consent).
• Behavioral Data: data about how you spend, how you repay, and your behavior towards finances.

B. Data Preprocessing

• The data collected is cleaned and standardized.
• Missing values are imputed and irrelevance data filtered out.
• This is to ensure that AI models can make accurate predictions.

C. Risk Modeling

• Machine learning algorithms are used in risk modeling to analyse the historical data of borrower to find out the patterns of defaulters.
• These techniques of data analysis include logistic regression, decision trees, random forest, gradient boosting, and neural networks.
• Borrowers are given a credit score or risk grade, which is used in deciding for who is eligible and what interest rate they will be charged.

D. Decision Engine

• AI is integrated with the loan origination system of the lender.
• Based on the risk score the system can:
  • Approve loans instantly
  • Approve with reservation of terms
  • Reject risky applications
• This automation reduces the amount of manual intervention to a massive degree.

E. Continuous Learning

• AI models are getting information about new data and repayment results continuously.
• This adaptive learning helps in enhanced accuracy over time and decrease in defaults.

4. Benefits of AI-Based Credit Underwriting

For Lenders

1. Faster Approvals – Instant evaluation makes the experience better for the customer.
2. Accurate Risk Prediction – By using AI, better identification of high-risk borrowers leads to a lower rate of defaults.
3. Operational Efficiency – Automating the processes reduces cost and enable for better scalability.
4. Regulatory Compliance – AI can be used to flag anomalies and suspicious applications automatically.
5. Portfolio Diversification – Lenders are free to make credit available to non-traditional borrowers.

For Borrowers

1. Quick Access to Credit – Loans can be sanctioned within a short period of time.
2. Inclusive Lending – Loans are made available to freelancers, gig workers and first-time borrowers.
3. Personalized Loan Offers – Based on the personalized risk factors to give personalized interest rate and EMI.
4. Reduced Paperwork – Most AI-driven platforms have little paperwork.

5. Examples of AI Use in India

Many fintech platforms and NBFCs of India are using AI for credit underwriting:

• CreditMitra: Uses advance risk model to analyze the borrower’s profiles in real-time for instant loan disbursal.
• MoneyTap: Uses machine learning to analyse the UPI and Bank transaction data to approve the personal loans instantly.
• Bajaj Finserv & Tata Capital AI automation of credit decisions, particularly in the case of digital lending products.
• Neo-banks and app-based lenders: Offer small ticket loans to new to credit users based upon alternative data analytics.

Result: Borrowers have been able to apply their mobile devices and get their application approved within minutes, sometimes with the money disbursed the same day.

6. Regulatory Considerations in India

AI in lending is bound to work under the RBI’s regulations:

A. Compliance of RBI Guidelines

• Only RBI-licensed NBFCs or banks are allowed to provide loans.
• Fintechs should collaborate with the regulated entities for legal lending.
• Interest rates, fees, and charges should be in accordance with RBI norms.

B. Data Privacy

• AI systems use sensitive data (bank statements, history of UPI Transactions, social media).
• Data Protection laws, such as the Personal Data Protection Bill (2023 draft) should be followed by platform.
• Borrower consent required before accessing and analyzing data

C. Responsible Lending

• AI models cannot promote over-borrowing.
• Lenders need to be fair in their collection processes as per RBI circulars.
• Transparency in credit decisions is important- it is important that borrowers have access to an understanding of why they were given a loan or denied one.

D. Bias and Fairness

• AI models should be designed in such a way that they do not lead to unintended bias.
• The RBI is expecting the lending companies to ensure that automated decisions do not discriminate based on age, gender, caste, or location.

7. Problems With AI-Based Underwriting

Despite the advantages, credit underwriting with AI has some challenges:

1. Data Quality: Data that is poor or incomplete can result in poor risk assessment.
2. Explainability: Some AI models are “black boxes” so it is hard to explain why a borrower was declined.
3. Regulatory Scrutiny: RBI is closely monitoring the digital lending practices, any non-compliance can lead to fines or suspension of the licence.
4. Cybersecurity Risks: AI platforms are handling sensitive financial data, and it is very important to protect the data from any kind of breaches.
5. Default Risk for New Borrowers: Although using alternative data is helpful, those with limited history are still more risky borrowers.

8. Best Practices for the Borrower using AI-Powered Loan Platforms

To receive the benefits of AI underwriting safely:

• Check RBI Compliance: Make sure that the lender is a registered bank or NBFC with RBI.
• Review Loan Terms: Even if your loan are approved quickly, check interest rates, tenure, and fees etc. 
• Maintain Financial Records: Passive financial records like accurate bank statements and history of UPI Transactions’s are good for higher chances of approval.
• Borrow Responsibly: Do not take upto multiple loans at one go; keep EMI within budget.
• Keep Track of Credit Score: AI systems often take into consideration CIBIL/other scores; one needs to keep the score good to get a low interest rate.

9. Future of AI in the Credit Underwriting

Using AI is not just the trend, its the future of lending. Important developments so far:

• Deeper Use of Alternative Data: AI is using more unconventional data such as e-commerce purchases, app usage and utility payments etc.
• Integration with Fintech Ecosystems: Lending will be integrated into various platforms such as e-commerce apps, travel apps, and wallets.
• Predictive Risk Management: AI will assist the lenders in predicting defaults and providing flexible restructuring options.
• Global AI Models: Indian lenders can use AI models employed globally to further perfect credit scoring.
• Ethical AI and Explainable Models: Transparency and fairness will be important parameters that will lead to its acceptance.

10. Key Takeaways

• AI-based credit underwriting involves the use of machine learning and data analytics to expedite, accurately, and inclusively credit underwriting.
• It’s beneficial for lenders, in terms of saving them money to reduce costs and defaults, and beneficial for borrowers to get instant access to credit without much paperwork.
• Regulatory Compliance is critical – only RBI-licensed banks or NBFCs can give loans legally. Fintechs have to play the role of partner or facilitator.
• Traditional and alternative data are used by AI models to make more accurate forecasts for the creditworthiness using alternative data such as UPI history, bank statements, digital footprints.
• Borrowers should be responsible with their borrowing and make sure to investigate the legitimacy of AI-powered lending platforms.
• The future holds more data-driven, inclusive and automated lending as where AI will shape the entire credit landscape in India.

AI-based credit underwriting is bringing a change in the personal lending scenario in India. It eliminates inefficiencies in manual processes, democratizes access to credit, and facilitates fast decisions. For the borrowers, the faster approvals, the personalized offers and the better financial inclusion are some of the benefits. To the lenders, this is a form of efficient risk management, cost savings and scalability.

When used responsibly and keeping within the ambit of assets set by the RBI, it can be said that AI-based lending platforms are redefining the face of credit in India with a safe, fast, and transparent ecosystem for borrowing.