India's alternative lending market lending built on data beyond traditional credit bureau history was valued at roughly $30.57 billion in 2025 and is projected to reach $52.3 billion by 2029, growing at a 14.4% CAGR (Source: ResearchAndMarkets, India Alternative Lending Databook). The RBI issued specific guidelines in 2024 governing the use of alternative data in lending decisions, formally recognizing what lenders were already discovering: traditional credit history alone isn't enough to serve India's borrower base.

The Internet of Things (IoT) the network of connected devices and sensors that exchange data in real time is one of the richer sources feeding this shift. For lenders, IoT data offers a way to see a borrower's actual behavior and asset condition, not just their historical repayment record. Unlike a credit bureau score, which reflects a snapshot of the past, IoT data keeps updating for as long as the loan is active.

This matters for a market where over 350 fintech and digital lending firms are competing to serve borrowers that traditional banks have historically underserved (Source: ResearchAndMarkets, India Alternative Lending Databook). The lenders that can assess risk more accurately, using richer and more current data, are the ones best positioned to serve this segment sustainably.

The lending industry's biggest constraint has never really been capital, it's been the ability to accurately assess risk for people the traditional credit bureau system doesn't see clearly.

Why Is Alternative Data Reshaping Lending in India?

India's lending ecosystem has grown substantially, fueled by traditional banks, NBFCs, and fintech startups. But structural challenges persist: high default rates in certain segments, inadequate or thin credit histories for large parts of the population, and a lack of personalized loan products that reflect an individual borrower's actual circumstances.

India is home to hundreds of millions of adults without a robust credit bureau file (Source: alternative credit scoring market research, 2026), which is precisely the population that alternative data including IoT-derived signals is best positioned to serve. Traditional credit scoring, built primarily on repayment history, simply has no data to work with for a first-time borrower.

Alternative data doesn't replace credit history, it fills in the picture for the large share of borrowers traditional credit history was never built to capture.

How Can IoT Improve Risk Assessment and Credit Scoring?

• Enhanced risk assessment: IoT devices can provide real-time data about a borrower's behavior and asset condition. For example, a lender financing a commercial vehicle can monitor usage patterns and maintenance signals, allowing risk to be evaluated on how an asset is actually being used rather than assumptions made at origination.
• Improved credit scoring: Traditional credit scoring often relies on limited data, leaving many potential borrowers underserved. Integrating data from utility payments, mobile usage, and connected-device signals can create alternative scoring models that broaden access to credit for individuals with limited or no formal credit history.
• Dynamic interest rates: With ongoing behavioral data, lenders can implement pricing models that adjust interest rates based on real-time risk signals rather than a static score set once at origination rewarding demonstrated responsible behavior over time.
• Personalized loan offerings: Data insights can enable lenders to offer loan products tailored to a borrower's actual circumstances rather than one-size-fits-all terms, improving both approval accuracy and customer satisfaction.
• Streamlined loan management: Connected devices can support more automated repayment tracking tied to asset usage or milestones, reducing default risk and simplifying the repayment experience for borrowers.

IoT's real value in lending isn't the novelty of the data, it's that the data updates continuously, instead of going stale the moment a loan is originated.

What Does IoT-Enabled Lending Look Like in Practice?

The clearest, most established example in India is vehicle and asset finance. Telematics devices GPS and usage trackers fitted to financed vehicles, tractors, and commercial equipment are already widely used by Indian NBFCs to monitor asset location and usage patterns over the life of a loan. This gives lenders real-time visibility that a paper-based loan file never could, and it directly supports faster, more confident lending in categories like commercial vehicle and equipment finance where the underlying asset is also the collateral.

This shift also changes how lenders think about risk over the life of a loan, not just at the moment of origination. A borrower's risk profile can improve or deteriorate over time, and IoT data makes it possible to recognize that shift as it happens rather than only at the next annual review which is what enables the dynamic, behavior-linked pricing models described above.

More broadly, the same principle extends to any lending decision that can draw on continuously updated, consent-based data: utility payment patterns, transaction-level cash flow, and device-based signals are all part of the same shift toward cash-flow-driven, real-time underwriting that Indian lenders are increasingly adopting alongside traditional credit history.

Asset-backed lending is where IoT has moved furthest in India because the asset being financed is also the sensor generating the data.

What Are the Risks and Limitations of IoT in Lending?

IoT-enabled lending isn't without real constraints, and lenders considering it need to weigh these carefully before scaling adoption.

• Data privacy and security: Integrating IoT into lending raises real concerns about data privacy and security. Lenders must comply with data protection regulations and implement robust cybersecurity measures to protect sensitive borrower information collected through connected devices.
• Infrastructure limitations: While urban India is increasingly adopting IoT technologies, rural regions still face connectivity and infrastructure gaps. Bridging this divide matters for ensuring IoT-driven lending benefits reach beyond metro markets.
• Regulatory clarity: India's regulatory approach to IoT-derived data in lending is still evolving alongside the broader alternative-data guidelines the RBI introduced in 2024. Clear, specific guidance on consent and data use for connected-device data will matter as adoption grows.
• Customer acceptance: While tech-savvy consumers may readily embrace IoT-based lending, others may hesitate to share device-level data. Lenders need to invest in clear customer education and demonstrate tangible value such as better rates or faster approval to encourage adoption.

None of IoT's risks are unique to lending they're the same consent, security, and infrastructure challenges every IoT use case faces, just with higher stakes given the financial data involved.

What Should Lenders Prioritize Before Adopting IoT Data?

IoT data is only as useful as the infrastructure built to govern and act on it responsibly.

1. Consent-first data collection. Borrowers need clear, explicit visibility into what device data is being collected and how it factors into lending decisions not data collection buried in fine print.
2. Explainable underwriting models. As IoT and other alternative data feed into automated risk scoring, lenders need to be able to explain and defend a credit decision, particularly when a loan is declined.
3. Secure, governed data pipelines. Real-time device data has to flow into underwriting systems through infrastructure that meets the same security and governance bar as core banking data not a separate, less-scrutinized pipeline.

This is closely connected to the kind of AI-ready data foundation that underpins any lender's ability to use real-time, alternative data responsibly at scale. At Bajaj Tech.AI, we help banks and NBFCs build the digital engineering and data infrastructure needed to bring new data sources like IoT into underwriting without compromising governance while helping guard against the kind of unregulated, poorly governed platforms behind the rise in fraudulent lending apps in India.

IoT data creates new lending opportunities only if it's built on the same governance discipline that responsible lending already requires.

Key Takeaways

• India's alternative lending market is projected to grow from $30.57B in 2025 to $52.3B by 2029, at a 14.4% CAGR
• IoT data helps lenders assess risk in real time rather than relying solely on a static credit history snapshot
• Vehicle and asset finance is where IoT-enabled lending has moved furthest in India, since the financed asset is also the data source
• Data privacy, infrastructure gaps, and regulatory clarity remain real constraints on IoT adoption in lending
• Consent-first collection, explainable underwriting, and governed data pipelines are prerequisites, not optional add-ons

Conclusion

The integration of IoT into India's lending sector presents a genuine opportunity for innovation — not as a replacement for sound underwriting, but as a richer, more current input into it. By leveraging real-time data responsibly, lenders can enhance risk assessment, broaden access to credit, and create more personalized borrower experiences. The lenders who treat data governance as a prerequisite, rather than an afterthought, will be the ones who turn this opportunity into durable, trustworthy lending at scale — and who capture the growth this market is projected to deliver over the next several years.

Exploring how alternative data can strengthen your lending models? Connect with our experts to build a governed data foundation for smarter underwriting.