AI-Driven Lending: Lessons from Emerging
Markets for Advanced Economies

The lack of access to formal credit is a source of frustration for many borrowers and small businesses. Conventional banks depend on papers, credit history, and old processes. AI-driven lending, in contrast, leverages new data and automation to serve the underserved. And it is developing countries that are setting the pace for that change. A combination of a high informal economy, rapid digital adoption, and unmet credit demand in India creates a unique opportunity and testbed. You yourself will discover how these markets innovate, what you should set as success factors, where the pitfalls lie that you want to pass on, and share, and what the developed economies can learn from this process.

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1. What is AI-Driven Lending?

Here’s what it means in practice:

• Lenders employ machine learning models to evaluate creditworthiness that is a bit more sophisticated than just relying on a credit score from a bureau.
• These models are powered by a variety of unconventional data sources: mobile usage, utility payments, e-commerce activity, wallet transactions, GST filings.
• Workflow automation: customer onboarding, document verification, underwriting, monitoring get accomplished quickly and at a lower cost.
• The objective: to access those borrowers who do not have a formal credit history and to be able to serve the risky segments at a cheaper price.

• India’s digital lending market is projected to reach $515 billion by 2030, up from $38.2 billion in 2021
• In the financial services sector, AI adoption in India reached around 68% in FY2024.

2. Why Emerging Markets Are a Useful Lens

Emerging markets share traits that force new models. You’ll learn from them because they innovate out of necessity.

Common traits of emerging markets:

• Large informal economy with many self-employed individuals.
• Many borrowers have no or thin credit history.
• Rapid smartphone and internet adoption.
• Underserved segments provide large latent demand.

India’s specific context:

• ~63 million MSMEs contribute ~30% of GDP and employ ~110 million people.
• India records very high monthly UPI transactions: a massive INR 27.28 lakh crore in October 2025.
• Fintech‐led digital lending in India grew at ~35% CAGR in 2024.
• For example, fintechs now hold ~52% share in India’s personal loan market.

Because India connects large demand + digital readiness + innovation, the lessons are especially relevant for advanced economies exploring how to serve new markets or digitise legacy systems.

Think about it:

• Want to handle a Black Friday spike without overspending on servers? Done.
• Need to keep pace with new card scheme rules or regulations? Much faster.
• Looking to add digital wallets or loyalty APIs? Cloud makes that plug-and-play.

3. Key Success Factors in AI-Driven Lending in Emerging Markets

These are the major ingredients you should focus on if you intend to follow this path.

1. Alternative Data & Credit Scoring Innovation

• Non-traditional data sources are recorded, for example, by the mobile recharge history, utility and bill payment patterns, e-commerce behaviour, and digital wallet usage.
• Such models are utilized by the lenders of emerging markets to develop predictive models that encompass those borrowers who are left out by the traditional credit bureaus.
• Example metrics: elevated approvals of thin-file borrowers, accelerated underwriting.

2. Table – Data Sources and Benefits

3. Digitisation & Automation of Workflows

• Onboarding: Verification of digital identities, e-KYC.
• Underwriting: Manual risk evaluations are replaced with AI scoring.
• Monitoring: Early detection of problems by real-time tracking of borrower activity.
• Some Indian lenders, for instance, cut the days-long time-to-disbursement to only a few hours.
• Efficiency gain: Banking sector in India could improve efficiency by ~46% with generative AI, as per an RBI report.

4. Financial Inclusion & Serving the Underserved

• Because cost is lower and process is digital, loans become viable for previously unserved segments.
• You reach first-time borrowers, rural micro-enterprises, informal workers.
• Inclusion becomes business growth, not just charity.

5. Regulatory & Ecosystem Support

• Aadhaar, UPI for payments, and Account Aggregator for data sharing are all essential elements of the digital infrastructure.
• Regulation systems: sandboxes, data protection, fairness rules.
• In India, fintech + policy alignment helped scale AI-lending quickly.

4. Lessons from Emerging Markets That Apply to India

Based on experience with developing markets, here are practical insights for Indian banks, fintechs, and governments.

1. Embrace Non-Traditional Data, With Care

• Data sources give new insight, but you must ensure fairness and privacy.
• Questions you must ask:
  • Do my models discriminate by gender/geography/income?
  • Is borrower consent clear when using their data?
  • Can you explain why a decision (approve/reject) happened?
• Use transparent model governance and audit trails.

2. Scale Digital Platforms with Efficient Risk Controls

• You must scale, but you cannot ignore risk.
• Recommended practices:
  • Real-time monitoring dashboards (default rates, fraud alerts).
  • Regular retraining of AI models to reflect changing patterns.
  • Use human-in-loop for edge cases.
• Example: Tier-III and rural markets saw higher delinquency in unsecured digital loans in India (over 4.2%).

3. Financial Inclusion as a Growth Driver

• As we already discussed, treat inclusion as market expansion, not charity.
• Step-wise approach: start with slightly underserved segments, then deeper pockets.
• Example: Digital lending is projected to capture ~53% of India’s fintech revenue by 2030 (~$133 billion).

4. Build Ecosystem & Policy Enablers

• Infrastructure: digital identity, payments, data ecosystems.
• Partnerships: banks + fintechs + data providers.
• Policy: ensure regulation stays adaptive, not stifling innovation.
• Example: 87% of enterprises deploy AI in some form; BFSI at ~68% adoption.

5. Challenges & Considerations for AI-Driven Lending in India

You should face these proactively if you are designing or scaling AI-lending operations.

1. Data Quality, Bias & Fairness

• Poor data leads to bad models.
• Bias risks: when certain groups (e.g., rural, women) are under-represented.
• Actions: audit data sets, track outcomes by demographic segment, correct for bias.

2. Regulatory & Compliance Issues

• Lenders must follow rules: transparency, explainability, borrower rights.
• RBI intervened recently with higher risk weights on unsecured consumer loans.
• You need governance: data protection, model documentation, grievance redressal.

3. Operational & Infrastructure Limitations

• Digital penetration is not uniform: rural areas may have connectivity or literacy issues.
• Hybrid models help: digital first + field support for edge segments.
• You must tailor your UX: multilingual, low-data, offline options.

4. Risk of Over-Optimism & Model Drift

• Models that work today may fail tomorrow if borrower behaviour changes.
• Set up model-monitoring: track key metrics (approval rates, default rates, turnaround time).
• Plan retraining cycles and conservative thresholds for new segments.

5. Ethical & Inclusion Trade-Offs

• An efficiency drive might turn you away from challenging-to-serve segments.
• You have to include statistics on inclusion, such as % first-time borrowers, % women borrowers, and % rural.
• Think about product design: reduced ticket prices and simpler conditions for the disadvantaged.

6. Strategic Recommendations for Indian Lenders & Policymakers

Here’s a roadmap you can implement.

For Lenders/Fintechs/Banks:

• Build shared data ecosystems across institutions to enrich models.
• Use modular AI platforms that adapt per borrower type (MSME, women entrepreneurs, gig-workers).
• Embed fairness audits and model governance from day one.
• Create borrower education programs: explain digital lending, rights, costs.
• Monitor and update models frequently, based on feedback loops.

For Policymakers & Regulators:

• Expand regulatory sandboxes for AI-lending innovations.
• Promote secure, consent-based data sharing frameworks.
• Encourage inclusive credit products via support/incentives.
• Develop AI ethics guidelines for finance: transparency, accountability, fairness.

For Ecosystem Builders:

• Use India’s digital stack: Aadhaar, UPI, DBT, Account Aggregators as foundations.
• Support fintech–bank partnerships: fintechs bring agility, banks bring trust & scale.
• Focus on vernacular UIs and rural access: design for inclusive adoption.

7. What Advanced Economies Can Learn (and India’s Role as a Bridge)

Emerging markets offer fresh lessons to more mature systems. India is already serving as a bridge between them.

Key takeaways for advanced economies:

• Use broader data beyond traditional credit history to reach new segments.
• Embrace digital origination and AI underwriting to reduce cost and friction.
• View financial inclusion as both social need and commercial opportunity.
• Build ecosystem support: public-infrastructure + fintech + regulation in alignment.

India’s role:

• Indian fintechs have scaled models fast and show what inclusion + technology look like.
• Advanced economies can modify those models to meet neglected populations in their own setting (e.g., gig workers, migrants, freelance economy).
• Cross-border collaboration: India can export frameworks, lenders can partner globally.

Wrapping Up

Millions are getting credit through AI lending. Emerging markets offer a way in which digital data, automation, and ecosystem enablement combine to serve underserved borrowers. India stands at the crossroads of inclusion and innovation.

If you are a lender, fintech, bank, or policymaker who wants to scale lending, shield against risk, and increase access, the time to act is now.

If you want assistance with AI-enabled lending platforms, risk analytics, or data ecosystem strategy building, Verinite has proficient domain knowledge. Get in touch with Verinite and see how your lending operation can be scalable, equitable, and future-ready.

FAQs

1. How does AI improve credit assessment?

It uses alternative data like mobile and GST records to judge creditworthiness beyond traditional scores.

2. Why is India leading in AI-driven lending?

Strong digital infrastructure and a large unbanked population push for faster adoption.

3. What are the key risks in AI lending?

Data bias, poor quality, and a lack of transparency in decisions.