Account Aggregator And Uli: Ai On India's Emerging Credit Infrastructure

India is building a more connected digital-credit infrastructure.

Two important layers are shaping this change:

• the Account Aggregator framework
• the Unified Lending Interface

The Account Aggregator framework enables consent-based sharing of financial information between participating regulated entities.

ULI provides standardized APIs that connect lenders with multiple data services used across the loan journey. RBIH describes ULI as a universal API gateway supporting identity verification, credit eligibility, income verification, asset information, documentation, and other lending services.

For banks and NBFCs, this changes how AI lending systems should be designed.

Instead of building AI around fragmented document uploads and disconnected integrations, lenders can increasingly design around structured, permissioned, and standardized data flows.

What the Account Aggregator Framework Enables

Account Aggregators connect customers, Financial Information Providers, and Financial Information Users through a consent-led data-sharing model.

RBI guidance explains that customers instruct the Account Aggregator to share financial information with authorized users such as lending banks or NBFCs, and the AA itself does not need to store the customer’s financial information.

For AI-led lending, this creates several useful capabilities.

Structured Financial Data for Underwriting

Traditional underwriting may depend on:

• uploaded statements
• PDFs
• manual verification
• document extraction

AA can provide lenders with digitally transmitted financial information where the required ecosystem coverage and customer consent are available.

This can reduce dependency on manual document handling.

For AI systems, structured data can support more consistent:

• underwriting
• affordability analysis
• cash-flow assessment
• risk evaluation

The important point is not that AA automatically improves every lending model.

It provides a more structured data rail on which those models can operate.

Better Financial Visibility

With valid customer consent, lenders may gain access to relevant financial information from participating providers.

This can improve the input available for:

• underwriting
• servicing
• credit monitoring
• segmentation

For AI, broader visibility can be valuable because model quality depends heavily on the quality and relevance of the data available.

But more data should not automatically mean more data collection.

Purpose and consent still matter.

Consent Becomes Part of the Architecture

One of the most important features of Account Aggregator is that consent is not merely an interface checkbox.

It is part of the data-sharing workflow.

AI systems using AA-derived data should therefore preserve:

• consent context
• purpose
• permitted data
• validity period

A credit model should not treat consent-backed information as unrestricted reusable data.

That would undermine the governance model the rail is designed to support.

Better Borrower Experience

Document-heavy lending can create friction.

Borrowers may otherwise need to repeatedly provide:

• bank statements
• supporting financial documents
• verification evidence

AA can reduce some of this duplication where the required data is available through the ecosystem.

RBI specifically notes that the framework can reduce loan-processing friction and support lending and credit-monitoring use cases.

What ULI Changes for Lending

ULI is now an operational lending infrastructure rather than simply a future concept.

RBIH describes it as a standardized API layer connecting lenders with multiple data-service providers across the loan journey.

As of 31 August 2026, RBIH reported:

• 136 lenders live
• 64 service providers live
• 143 services live

across the platform.

This matters because lenders no longer need to create every data integration independently.

ULI Reduces Integration Fragmentation

A lender may need several types of information during a credit journey.

Examples include:

• identity verification
• credit bureau information
• income verification
• asset data
• property information

ULI standardizes access through a common API infrastructure.

RBIH describes the platform as a bridge between lenders and data-service providers using standardized schemas and APIs.

For AI systems, that creates a cleaner integration boundary.

Instead of every model being tied directly to dozens of providers, the lending architecture can interact with a standardized service layer.

Account Aggregator and ULI Are Complementary

AA and ULI should not be treated as interchangeable technologies.

They solve different parts of the lending architecture.

A useful way to think about them is:

Account Aggregator

→ consent-led financial information sharing

ULI

→ standardized lending-service and data-access infrastructure

ULI itself includes Account Aggregator within its available income-verification ecosystem.

Together, they can support cleaner data access across digital-credit journeys.

How AI Should Be Designed Around These Rails

AI should not sit outside the infrastructure and recreate functionality already being standardized underneath it.

A better architecture is:

Consent / borrower authorization

→ AA / ULI data access

→ data validation

→ AI underwriting or risk model

→ policy / governance layer

→ human or automated lending decision

This creates clearer separation between:

• data access
• model intelligence
• decision policy
• governance

Use Structured Data as a Primary Input

Where AA or ULI-supported services provide the necessary information, AI systems can use those structured sources as primary inputs.

Legacy workflows such as:

• manual uploads
• document parsing
• bilateral integrations

can remain fallback mechanisms where necessary.

But they should not automatically define the target architecture.

AI Underwriting

AI underwriting can analyze relevant financial signals to support credit decisions.

This may include:

• cash-flow patterns
• income indicators
• repayment behavior
• existing obligations

AI lending can become more effective when data acquisition and model decisioning are clearly separated.

AA and ULI provide infrastructure.

The AI model provides analysis.

The credit policy still determines what action is permitted.

Fraud and Risk Intelligence

Better data infrastructure can also support fraud and risk controls.

AI fraud detection may combine:

• identity signals
• account data
• bureau information
• application patterns

to identify anomalies requiring review.

However, access to additional data does not remove the need for:

• validation
• model monitoring
• explainability
• human escalation

Respect Consent and Purpose Limitation

This is one of the most important design requirements.

Data obtained through a consent-led framework should be used according to the approved purpose and applicable rules.

AI teams should not assume that because data is technically available, it can be reused freely across:

• marketing
• unrelated scoring
• profiling
• future model training

Consent context should travel with the data.

That makes governance an architectural requirement.

Separate Data Access From Model Training

Another important distinction is between using data for a current lending decision and using it to train future AI models.

These are not necessarily the same purpose.

Enterprise AI teams should have explicit controls over whether consent-backed operational data can enter:

• training datasets
• evaluation datasets
• analytics systems
• persistent customer profiles

AI Governance and Compliance becomes especially important at this boundary.

Build for ULI Evolution

ULI already supports a broad and growing service ecosystem, and RBIH documentation shows ongoing expansion of service categories and next-generation APIs.

Lending architectures should therefore avoid hard-coding AI systems around one fixed data model.

Use:

• API abstraction
• schema versioning
• modular integrations
• configurable policy layers

This makes it easier to adopt new services without redesigning the entire credit stack.

Design for Exceptions

Neither AA nor ULI eliminates every edge case.

Lenders still need fallback logic for situations where:

• required data is unavailable
• a customer does not consent
• records are incomplete
• verification fails
• an API is temporarily unavailable

AI systems should not silently fail or fabricate missing information.

The workflow should instead route the case toward:

• alternative data
• additional documentation
• manual review

Governance Around AI Credit Decisions

Better infrastructure does not eliminate model risk.

AI used for lending still needs controls around:

• model inputs
• decision rules
• bias
• explainability
• monitoring
• overrides

The lender should be able to reconstruct:

Which data was used?

Which model generated the output?

Which policy converted that output into a decision?

This is particularly important for adverse or high-impact credit outcomes.

Observability and Auditability

A production AI lending architecture should preserve logs for:

• consent
• data access
• model version
• model output
• policy decision
• human override

This allows teams to investigate decisions later.

AA and ULI can strengthen the input and integration layers.

The lender still needs end-to-end governance around the complete decision workflow.

What Has Changed From Earlier AI Lending Architecture

Earlier AI-led lending programs often depended heavily on:

• uploaded PDFs
• manual bank-statement processing
• custom integrations
• separate verification services

India’s digital-credit infrastructure is making a more standardized approach possible.

AA supports consent-based financial information sharing.

ULI provides a common lending API layer across multiple data services.

RBIH’s current implementation shows that ULI now spans services across credit eligibility, asset verification, identity verification, income verification, documentation execution, and value-added lending functions.

That changes where AI should sit in the architecture.

AI increasingly becomes the intelligence layer above standardized digital rails.

How Mobiloitte Can Support AI-Led Lending Architecture

Mobiloitte supports BFSI organizations across:

• AI lending
• credit analytics
• fraud detection
• API integration
• data architecture
• AI governance
• workflow automation

BFSI solutions can provide the broader financial-services implementation layer.

For institutions building on AA and ULI, the objective should be to create an architecture where:

trusted data access

• AI intelligence
• policy controls
• auditability

work together.

Conclusion

Account Aggregator and ULI are changing how AI-led lending can be designed in India.

AA provides a consent-led financial-data sharing framework.

ULI provides standardized APIs connecting lenders with a growing ecosystem of lending data and services.

Together, they can reduce dependency on fragmented document and integration workflows.

But the real advantage does not come from connecting AI to more data.

It comes from building AI around:

• explicit consent
• standardized infrastructure
• clear data purpose
• model governance
• auditable decisions

The strongest lending architectures will treat AA and ULI as infrastructure layers from the beginning rather than optional integrations added after the AI model is built.

Talk to Mobiloitte About AI-Led Lending Solutions

FAQs

1. What is the Account Aggregator framework?

It is a consent-based financial data-sharing framework that allows customers to authorize financial information to be shared between participating Financial Information Providers and Financial Information Users.

2. What is ULI?

The Unified Lending Interface is an RBIH platform that provides standardized APIs connecting lenders with multiple data and service providers across the lending journey.

3. Is ULI still only a pilot or emerging concept?

No. RBIH reports that, as of 31 August 2026, ULI had 136 live lenders, 64 live service providers, and 143 live services, although the ecosystem continues to expand.

4. How does Account Aggregator help AI underwriting?

It can give lenders access to relevant, digitally shared financial information with customer consent, reducing reliance on some manual document workflows.

5. Are Account Aggregator and ULI the same thing?

No. AA focuses on consent-led financial information sharing. ULI is a broader lending API infrastructure that connects lenders with multiple data services, including Account Aggregator-based income-verification services.

6. Can AI reuse Account Aggregator data for any purpose?

It should not be treated as unrestricted data. AI use should stay aligned with the applicable consent, purpose, governance, and regulatory requirements.

AI Governance and Compliance can support this broader control layer.

7. How does ULI help lenders integrate data?

ULI provides standardized APIs and schemas through which lenders can access multiple participating services rather than building every bilateral integration separately.

8. Where does AI sit in an AA/ULI lending architecture?

AI should sit above the data-access layer, using relevant information for underwriting, risk, or servicing while remaining subject to credit policy, consent, governance, and audit controls.