India's Account Aggregator framework is shifting loan underwriting toward real-time, data-backed AI models. As of late 2025, 64 lenders have integrated with the Unified Lending Interface, facilitating over ₹1.47 trillion in credit. While this allows for faster and more precise loan decisions, concerns persist regarding data usage transparency and the lack of strict purpose limitations.
Detailed Coverage
The landscape of Indian retail lending is shifting as the Reserve Bank of India’s Account Aggregator (AA) framework gains traction. By moving away from static documents like bank statements and salary slips, the system allows borrowers to provide real-time, consented access to their financial data. This flow of information is fueling AI-driven underwriting models that go beyond traditional credit scores to assess a borrower’s actual cash flow, spending habits, and repayment capacity.
Scaling Digital Lending
The adoption of this digital infrastructure has been notable. By December 2025, 64 lenders had linked with the Unified Lending Interface, using over 136 different data services to refine their risk assessments. Data from Sahamati, the ecosystem’s self-regulatory body, indicates that the network enabled approximately ₹1.47 trillion in loans between April and September 2025. Personal loans now account for nearly 10% of this total volume, signaling that the system is successfully being used to streamline smaller, high-frequency credit products.
Precision Over Paperwork
For borrowers, particularly the self-employed or those with irregular income streams, this transition offers a more nuanced evaluation. Traditional credit scoring often fails to capture the full financial health of non-salaried individuals. By examining mutual fund holdings, income consistency, and existing debt obligations in real time, lenders can create more tailored credit offers. This granular approach potentially increases approval rates for creditworthy individuals who might otherwise be rejected by automated systems relying solely on generic credit bureau data.
Addressing Data Governance Risks
The shift toward automated, AI-based lending has drawn attention to data privacy and usage. A primary concern for industry observers and technology experts is the current lack of strong purpose limitation. Once a lender gains access to a borrower's financial profile, ensuring the data is used strictly for loan underwriting—and not repurposed for targeted marketing or aggressive collection efforts—remains a complex challenge for regulators.
Industry participants have pointed out that many borrowers often grant consent for convenience, without fully realizing the implications for their data privacy. To bridge these gaps, the Reserve Bank of India has proposed regulations that would force lenders to be more transparent about how their AI models arrive at specific loan decisions. Simultaneously, Sahamati has introduced a framework utilizing confidential computing, which aims to provide borrowers with a clearer record of exactly how their data was processed.
Looking ahead, the effectiveness of these measures will depend on implementation. Until standardized regulations are fully in place, borrowers are encouraged to stick to lenders recognized by the RBI, exercise their right to revoke consent on AA applications after loan processing, and request data deletion after the credit relationship concludes. Investors and stakeholders will likely monitor the transition toward these more transparent data-processing frameworks, as they will define the long-term reliability and public trust in digital lending infrastructure.