Can India’s ULI do a consent-based turnaround for financial inclusion?
Two nations, two philosophies, one challenge: universal credit accessibility.
In the grand laboratory of digital governance, both India and China perhaps represent the most compelling natural experiments of our time. Both nations face the same fundamental challenge of financial inclusion for billions of citizens who largely exist outside of the traditional credit system. Yet, their chosen paths could not be more different. It is a case study on data governance, state power, and trade-offs between efficiency and individual autonomy.
China has built what many would call a surveillance state to address credit access issues, while India is relying on consent and collaboration. As the Unified Lending Interface (ULI) transitions from a revolutionary pilot to a nationwide rollout, the question is not about privacy versus surveillance; it is whether India’s polite approach can actually work at scale.
China’s Compulsion Model
The Western narrative of China's Social Credit System as a dystopian "Black Mirror" episode has obscured a more nuanced reality. China’s credit system remains a fragmented, business-focused framework with limited digital sophistication. By 2023, there was no unified score for individuals; instead, enforcement is carried out through targeted blacklists for tangible infractions like debt default.
Ironically, despite its disjointed and low-tech nature, the system has proven effective, covering over a billion people and enabling billions of credit checks, largely because of China's authoritarian capacity to enforce data-sharing and consequences. This underscores a troubling truth: China's strength lies not in brute technological prowess but in its ability to mandate compliance, raising concerns about unchecked state control rather than innovative governance.
China’s social credit system is blunt-force data aggregation. Every digital footprint of the citizen, every transaction, every social interaction is fed into a centralized scoring mechanism that determines creditworthiness. It is invasive, it is comprehensive, but it undeniably works. Citizens with a good score get instant loans, preferential rates, and streamlined approvals. Those without a shiny profile often suffer difficult consequences.
China solves this through state compulsion: institutions participate because they must, not because they want to. Data flows upward to centralized databases because regulatory frameworks mandate it, and consequences for non-compliance are severe.
India’s Architecture of Politeness, a.k.a Consent Model
India’s Unified Lending Interface is quite a contrast. It is, in a way, digital governance with good manners, seeking consent at every step. It is built on the foundation of the Account Aggregator framework, which includes explicit consent, standardized APIs, and privacy-first data sharing.
The promise is lucrative. Democratize credit access without sacrificing individual privacy.
However, the theory could collide with harsher realities in its implementation and scale.
The success of implementing ULI is dependent on how quickly it can unravel the complex data sources jigsaw. The ULI ecosystem depends on a convoluted web of data sources that currently operate in silos. Land records from the state government, GST data from tax authorities, banking transactions from FIPs, utility payments from different service providers, business performance data from e-comment platforms, and more. Each of these data sources has its own governance structure, its own incentive framework, and its own reason for either participation in the ecosystem or staying away.
Let us take an example of land records, which are crucial for agricultural lending. Most state governments maintain these records in a legacy system with questionable data quality. For ULI to work, these states need to first digitize, standardize, and then agree to share this data through APIs. And what is their incentive to do so? The upfront cost of digitizing and standardizing these data sets is significant, the technical complexity is real and immediate benefits for the state government are unclear. We have seen this movie before with GST implementation, initial enthusiasm followed by implementation chaos.
However, like AA and GST – it’ll ultimately take political will and a strong policy stance to ensure that not just these disparate actors fall in line but also abide by the spirit and letter of the framework.
The GST data story is particularly instructive. While GST Sahay has shown promise in pilot mode, the broader challenge remains data quality and manipulation. Small businesses routinely underreport revenues for tax purposes, and this same manipulated data becomes the foundation for credit decisions in a ULI-powered system.
China's surveillance model catches such discrepancies through cross-verification across multiple data points, while India's consent-based model assumes borrowers will voluntarily share accurate data. A tough battle to win.
The AA Monetization Lesson
Meanwhile, there is a bubbling incentives/monetization problem that is deterring Account Aggregator adoption. It will hold important lessons for even ULI when that rubber meets the road.
Data holders need a clear revenue model to participate meaningfully in the ULI ecosystem. Banks share transaction data, but what is their incentive when another NBFC uses that data to underwrite a loan and earns from it? E-commerce platforms provide seller performance data, but how do they benefit when that data helps secure working capital for their merchants? Without solving the monetization puzzle, ULI risks becoming another well-intentioned infrastructure project with limited real-world adoption.
The Governance Challenge
The governance challenge with ULI is even more complex as it requires multiple regulators to coordinate seamlessly. RBI for financial data, IT ministry for digital infrastructure, state governments for land records, and the GST network for tax data. Each operates under different timelines, priorities, and regulatory frameworks.
China's system has become an economic passport, linking everything from digital yuan transactions to tax compliance to social behavior monitoring. Citizens navigate a world where their economic identity is inseparable from their civic identity. India's approach promises a similar integration but through voluntary aggregation. China’s social credit system defies this systemic challenge by operating on a centralized decision-making model, and India’s federal structure makes such coordination exponentially harder.
Scale vs. Pilot Reality
The ULI platform had 27 lenders, 12 unique loan journeys, including KCC loans, dairy loans, unsecured MSME loans, personal loans, and home loans, with 54 APIs, 5 AAs, and many banks participating in the pilot.
These numbers sound impressive until you realize India has thousands of NBFCs, hundreds of fintech lenders, and countless data sources that could enhance credit decisions. Scaling from a controlled pilot environment to a nationwide ecosystem where every participant has aligned incentives is the real test.
China's approach sidesteps these challenges through compulsion. India's approach requires voluntary participation at every level, from borrowers sharing their data to institutions building API connections to regulators standardizing frameworks across jurisdictions. It is a coordination challenge that dwarfs most previous digital infrastructure projects.
The ₹80 trillion unmet demand for MSME credit is not just a market opportunity: it is a policy imperative. But good intentions and elegant technical architecture are not enough. ULI's success depends on solving the same incentive and governance challenges that have plagued every previous attempt at building open data networks in India.
The big picture question isn't whether ULI's privacy-first approach is morally superior to China's surveillance model. The question is whether India can build a data-sharing ecosystem that works without the stick of state compulsion. The pilot results are encouraging, but the real test begins when ULI has to coordinate hundreds of institutions, thousands of data sources, and millions of borrowers, all while maintaining the delicate balance of incentives that keeps everyone participating voluntarily.
If it works, ULI could become a template for privacy-respecting financial infrastructure globally. If it doesn't, the ₹80 trillion credit gap will persist, and we'll be left wondering whether being polite about data was worth leaving millions of businesses without access to formal credit.
Let me know your thoughts on how ULI will shape the Indian credit ecosystem.
Until next time,
Rajat