AI-Based Credit Scoring in 2026: Transforming Lending Decisions

Artificial Intelligence (AI) is revolutionising credit scoring in India, particularly for MSMEs. The shift from traditional bureau scores to advanced data-driven models is enabling businesses to access credit faster and with fewer hurdles. MSMEs now experience reduced paperwork and quicker decisions, which is vital for growth.

AI in credit scoring leverages behavioural data and digital footprints to assess creditworthiness more accurately. This transformation is also influencing Home Loan and Business Loan approvals, making financial inclusion more achievable.

What Is AI-Based Credit Scoring?

AI-based credit scoring evaluates borrowers using machine learning algorithms and behavioural data rather than relying solely on historical credit bureau scores. It analyses digital footprints, transaction patterns and GST data to create a holistic credit profile. In India, the rise of digital payments and GST compliance has strengthened AI scoring models, making them more reliable. These systems are particularly beneficial for MSMEs seeking Business Loan solutions, as they provide fairer assessments for those with limited credit histories.

Also Read: What is Business Loan: Complete Guide

How Does AI-Based Credit Scoring Work?

AI-driven credit scoring uses diverse datasets such as GST returns, bank statements, transaction histories and UPI patterns. These inputs allow algorithms to detect patterns, predict risks, and score beyond traditional bureau data. Key capabilities include:

• Pattern detection for spending and repayment behaviour: AI models analyse historical transaction and cash flow data to identify trends in income stability, expense management and repayment discipline over time. These patterns help lenders form a more dynamic view of credit behaviour compared to static, point-in-time assessments. However, such insights are used as decision-support tools and are typically combined with established credit parameters to ensure prudent and compliant lending.
• Fraud risk identification through anomaly detection: AI systems use anomaly detection techniques to flag deviations from a borrower’s usual financial behaviour, such as irregular transaction volumes, inconsistencies in declared income or unusual payment activity. These indicators help lenders strengthen fraud monitoring and early risk detection. Importantly, AI-generated alerts are generally reviewed alongside human oversight and other control mechanisms to avoid false positives and ensure responsible risk management.
• Scoring beyond conventional credit bureau data for inclusivity: For borrowers with limited or no credit history, AI enables the use of alternative data to supplement traditional bureau scores. By analysing cash flow patterns, digital payment behaviour and business transaction histories, lenders can better assess creditworthiness of underserved segments. This approach supports financial inclusion while remaining subject to data privacy norms, customer consent and regulatory compliance.

Traditional Credit Scoring vs AI-Based Credit Scoring

Traditional credit scoring relies heavily on historical repayment data, which often disadvantages MSMEs with thin credit files. AI models, however, incorporate alternative data sources, improving accuracy and reducing bias. Key differences include:

• Speed: AI provides real-time scoring compared to slower traditional methods
• Accuracy: AI reduces human error and bias
• Data sources: AI uses GST, UPI, and digital footprints versus limited bureau data
• Inclusivity: AI benefits MSMEs and new-to-credit borrowers

Key Benefits of AI-Driven Credit Scoring

AI-driven credit scoring offers numerous advantages for MSMEs and individuals seeking home loans or business loans. These include:

• Faster loan approvals, reducing waiting times
• Lower documentation requirements
• Enhanced risk prediction for lenders
• Greater access for new-to-credit MSMEs
• Integration of alternative data for comprehensive scoring

2026 Trends: How AI Credit Scoring is Shaping MSME Lending

By 2026, AI credit scoring will dominate MSME lending in India. Key trends include:

• Machine-learning scoring models for NBFCs: It is likely that many NBFCs will be using machine-learning scoring models that combine traditional bureau inputs with alternative datasets. These models can exceed the traditional rule-based scoring, learning from historical patterns to improve predictive accuracy for risk and repayment performance.
• Embedded lending within e-commerce platforms: E-commerce marketplaces and B2B platforms are integrating financing offers directly into the purchase and procurement journey, allowing small merchants to access working capital or invoice financing at the transaction point.
• Advanced AI-based fraud detection: These systems augment traditional rule-based controls, helping lenders reduce fraud losses and protect portfolio quality without slowing down genuine approvals. Continuous learning allows these models to adapt to evolving fraud patterns.
• Real-time credit underwriting: By integrating predictive risk models and alternative data feeds, lenders can evaluate MSME credit applications and deliver decisions within minutes, rather than days. This capability is supported by scores delivered via APIs that combine bureau and behavioural insights, reducing total time-to-credit and enhancing the borrower experience.
• Integration with India Stack, including Aadhaar, GST and UPI: Consent-based access to Aadhaar e-KYC, GST filings, and UPI transaction data, together with Account Aggregator frameworks, enables lenders to securely gather rich financial profiles quickly. This integration reduces documentation friction, speeds onboarding and enriches AI models with high-quality, verified data for more inclusive scoring.

AI Credit Scoring: Challenges & Limitation

Despite its significant advantages, AI-based credit scoring also presents several challenges that lenders must address carefully. The use of large volumes of customer data raises important data privacy and consent concerns, particularly when alternative data sources are involved. There is also the risk of algorithmic bias if models are trained on incomplete or skewed datasets, potentially leading to unfair credit outcomes. In addition, alternative data can be misinterpreted without proper context, resulting in inaccurate risk assessments. These challenges are further compounded by evolving regulatory oversight and compliance requirements, which demand transparency, explainability and robust governance frameworks to ensure responsible and ethical use of AI in credit decision-making.

How MSMEs Can Improve Their AI Credit Score

MSMEs can enhance their AI credit scores by:

• Maintaining digital payment hygiene
• Filing GST returns on time
• Ensuring stable business bank transactions
• Using accounting tools for consistent data management

Also Read: What Can You Do to Improve Credit Score

Final Thoughts

AI is reshaping MSME credit access and improving financial inclusion. Businesses that adopt digital financial behaviours will benefit from faster approvals and fairer assessments. For MSMEs and individuals seeking Business Loans, embracing AI-driven systems is no longer optional but essential for growth.

Apply now for a Business Loan.

FAQs

Q.1. Can AI improve loan approval chances for new-to-credit MSMEs?

A. Yes. AI helps assess MSMEs with limited credit history by using alternative data, improving access to credit while managing risk.

Q.2. What data does AI use for credit scoring that traditional credit bureaus do not?

A. AI can analyse bank transactions, GST data, UPI behaviour and cash flow patterns, in addition to bureau scores, subject to consent.

Q.3. Is AI-based credit scoring approved by Indian regulators?

A. Yes, AI use is permitted when it follows RBI guidelines on data privacy, transparency, consent and responsible lending.

Q.4. Are AI credit scores more accurate than traditional scores?

A. They can be more predictive for thin-file borrowers, but work best when combined with traditional credit assessment methods.

Q.5. Does AI credit scoring reduce loan fraud?

A. Yes. AI helps detect anomalies and suspicious patterns early, strengthening fraud prevention and risk monitoring.

Disclaimer:

The content presented on this page, including images and factual information, is intended solely as a summary derived from publicly available sources. GHFL/GFL (“Company”) does not claim ownership of such information, nor does it represent that the Companies have exclusive knowledge of the same. While efforts are made to ensure accuracy, there may be inadvertent errors, omissions, or delays in updating the content. Users are strongly encouraged to independently verify all information and seek expert advice where necessary. Any decisions made based on this content are solely at the discretion and responsibility of the user. Godrej Capital and its affiliates assume no responsibility for any loss or damage that may result from the use of or reliance on the information provided herein.