RBI Governor Shaktikanta Das announced that the Unified Lending Interface (ULI) will soon be introduced to facilitate frictionless credit delivery.

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• Pilot Project: Continuing on the journey of digitalisation of banking services, the pilot project for a public tech platform for frictionless credit facility was announced in August last year.
• Transforming Role: ULI will play a similar role in transforming the lending space in the country, just as United Payment Interface (UPI) revolutionized the payment ecosystem.

Features of Unified Lending Interface

• Unified Platform: ULI provides a consolidated tech platform for frictionless credit delivery, akin to UPI’s role in payments.
• Standardized Application Programming Interface (APIs): It has common and standardized APIs designed for an approach to ensure digital access to information from diverse sources, i.e., information relevant to a lender giving out a loan to a potential borrower.
• Seamless Data Flow: Facilitates the seamless, consent-based flow of digital information, including land records, from various service providers to lenders.
• Enhanced Credit Appraisal: Reduces the time and complexity of credit appraisal by integrating financial and non-financial data from disparate systems like Central and State governments, account aggregators, banks, etc.
• Reduced Documentation: Enables quicker credit turnaround with minimal documentation requirements.
• Consent Based Structure: The entire system is consent-based, i.e., based on the consent of the potential borrower.

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Significance of ULI

• Improved Accessibility: Addresses large unmet credit demand, especially for agricultural and MSME sectors.
• Streamlined Processes: Enhances efficiency in credit delivery and reduces operational complexities for lenders.
• Empowers Innovation: Supports fintechs and lenders in creating tailored lending solutions by providing access to comprehensive data sources.

Challenges to the Unified Lending Interface

• Data Privacy: AI integration raises concerns about handling large volumes of personal data and potential breaches of privacy.
• Bias and Authenticity: The risk of biases in AI models and the authenticity of training data need to be carefully managed.
• Ethical Governance: Ensuring that AI models are explainable and free from biases is crucial to maintaining fairness and accountability in decision-making.
• Technical Complexity: Managing the integration of various data sources and ensuring seamless technical operations.
• Regulatory Compliance: Adhering to regulatory requirements while implementing new technologies and ensuring data integrity.

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