India is undergoing one of the most significant shifts in its financial data architecture. As of 2023, over 1.1 billion bank accounts have been enabled for secure data sharing through the Account Aggregator (AA) framework, according to the Reserve Bank of India’s Account Aggregator ecosystem progress reports (RBI, 2023).
This development marks a structural turning point in how financial data is accessed and exchanged in the lending ecosystem. It signals the transition from institution-controlled data visibility to user-authorised, purpose-specific, and revocable data sharing.
At the same time, India’s credit landscape is expanding towards segments that have historically been difficult to underwrite: first-time borrowers, gig economy earners, small merchants, and micro-entrepreneurs. These individuals often possess financial capacity but lack a bureau-visible footprint. As a result, traditional credit scoring models are insufficient for evaluating their creditworthiness.
For the past decade, alternate data has been viewed as the solution to this gap. Transaction histories, GST filings, platform payouts, and digital payment behaviour can together present a more accurate and contemporary view of repayment ability. However, alternate data has faced a persistent challenge: it has been fragmented, inconsistent in quality, and difficult to access transparently.
Consent-based data sharing changes this dynamic!
By standardising access, ensuring user control, and enforcing purpose-bound usage, the AA framework and the Digital Personal Data Protection (DPDP) Act together create the conditions under which alternate data can be reliably used in credit decisioning. What was previously informal and operationally complex can now be analysed in a structured, compliant, and auditable manner.
This shift represents a redefinition of how creditworthiness is established. The emphasis moves from historical participation in the credit system to verified, real-time indicators of financial behaviour. As a result, consent-based data sharing is poised to become the foundation upon which the next phase of India’s alternate data–driven credit ecosystem is built.
Why Alternate Data Could Not Scale Before Consent-Based Sharing
Alternate data has been discussed in India’s credit ecosystem for many years. Lenders have long recognised that transaction patterns, cash-flow behaviour, and platform-based earnings are often stronger predictors of repayment capability than historical credit records alone. However, awareness did not translate into adoption. The limitations were not conceptual, but structural.
1. Fragmented Data Sources and Inconsistent Formats
Alternate data originates from multiple financial and digital systems: bank accounts, payment apps, GST platforms, gig platforms, e-commerce records, and more.
Before the AA framework, obtaining this data required different access methods, each with its own format, frequency, and reliability. This resulted in:
- High operational effort to aggregate and standardize data
- Inconsistent data quality across customer cohorts
- Difficulty in automating underwriting workflows
The lack of standardization highly limited scalability!
2. Dependence on Customer-Provided Documents
In the absence of formal data-sharing rails, lenders often relied on uploaded PDFs, screenshots, or email-transferred statements. These methods introduced verification risks since bank statement PDFs could be altered, screenshots lacked traceability, Email-sent documents created storage and compliance liabilities etc. This meant alternate data was available, but not always credible.
3. Limited Transparency for the Borrower
Before consent sharing entered the picture, borrowers often did not have clarity on what data was being shared, who had access to it how long it would be stored and for what purpose it would be used. This created a trust deficit. Borrowers shared data because they had to, not because they were empowered to. Such implicit consent was unsuitable for a system expected to scale.
4. Compliance and Data Privacy Risks for Lenders
Prior to the establishment of standardised consent governance, lenders had to shoulder the burden of securing data transfers, storing sensitive information, demonstrating consent validity and managing withdrawal or modification of consent. Without a consistent regulatory framework for data usage, the risk of liability outweighed the strategic value of alternate data for many institutions.
The result of these 4 factors meant that alternate data remained a high-potential, low-implementation asset. Its predictive value was recognised, but the lack of infrastructure prevented it from being used reliably due to quality variability, responsibly due to unclear consent norms and at scale due to operational burdens.
To unlock its real value, alternate data needed a trusted, standardised, and user-controlled data-sharing foundation.
How Consent-Based Data Sharing Enables Scalable and Trustworthy Alternate Data
The shift toward consent-based data sharing directly addresses the challenges that previously limited the use of alternate data in credit scoring. The Account Aggregator (AA) framework introduces a standardised, secure and user-permissioned mechanism for sharing financial information across institutions. Instead of lenders relying on static documents or fragmented data channels, the AA framework allows borrowers to authorise access to real-time financial data from regulated Financial Information Providers (FIPs). This creates a consistent and verifiable foundation for analysing bank transactions, income flows, digital payment patterns, and other forms of alternate data. It transforms data access from a manual, trust-dependent process into a structured workflow.
A defining feature of the AA model is its emphasis on purpose-bound and time-bound consent. Borrowers are informed of exactly what data is being accessed, for what purpose, and for how long, and they retain the right to withdraw this access at any point. This approach aligns directly with the consent provisions under the Digital Personal Data Protection (DPDP) Act which requires that financial data sharing be specific, informed, and revocable.
For lenders, this clarity reduces compliance uncertainty and ensures that alternate data in underwriting is supported by a traceable consent artefact. For borrowers, it reduces information asymmetry and builds trust in the system. In effect, consent becomes a governance mechanism!
This infrastructure enables lenders to apply cash-flow based underwriting and behavioural analysis with far greater confidence. Real-time transaction insights allow lenders to assess income stability, spending discipline, savings behaviour, credit utilisation trends, and repayment consistency even for thin-file borrowers who lack bureau histories. For small businesses and micro-entrepreneurs, GST filings, platform payouts, and current account flows become tangible proof of operating capacity.
This means that creditworthiness can be evaluated based on how financial behaviour is demonstrated today, rather than whether the borrower has previously interacted with formal credit channels. Alternate data shifts from being anecdotal to institutional-grade evidence.
At a system level, the combination of AA-enabled data portability and DPDP-driven privacy governance lays the groundwork for a more inclusive credit ecosystem. Lenders gain access to richer, more contextual data while maintaining regulatory compliance. Borrowers gain agency over their financial information and the ability to demonstrate credibility without prior credit interactions.
The result is a more equitable and efficient market: credit decisions become faster, pricing becomes more accurate, and access expands to segments traditionally overlooked. Consent-based data sharing legitimises alternate data, ensuring that the future of credit is built not only on intelligence, but on transparency and trust.
The Future of Alternate Data: Expanding Data Domains and the Rise of Continuous Scoring
The next phase of India’s credit evolution will be defined not only by the availability of alternate data, but by the breadth of data sources that become interoperable and the frequency at which this data can be assessed. The Account Aggregator (AA) ecosystem is still in its early stages, but its foundational architecture is designed to accommodate new categories of financial and quasi-financial information over time. As additional sectors integrate, the range of signals available to lenders will expand, improving both the accuracy and inclusivity of credit decisioning.
In the near term, insurance, securities, and pension data are expected to join bank and GST information within the AA framework. Insurance premium payment regularity, for instance, can serve as a strong indicator of long-term financial discipline. Similarly, systematic investment plan (SIP) contributions or demat account activity can highlight savings behaviour and wealth stability. For micro and small businesses, expanded access to invoice-level trade data and supplier payment histories will provide deeper insight into cash-flow robustness. Each of these data domains enhances the lender’s ability to assess repayment capacity through evidence-based behavioural markers as opposed to assumptions based on formal employment or past credit usage.
However, the more significant shift lies in how frequently data is assessed. Traditional underwriting evaluates creditworthiness at a single point in time: the moment of application. With consent-based access and borrower-controlled data sharing, underwriting can transition from a static evaluation to continuous scoring where financial health is monitored in real time. This model benefits both lenders and borrowers. Lenders can proactively identify early signs of repayment stress and take corrective action, reducing delinquencies. Borrowers, on the other hand, can benefit from dynamic credit limits, better pricing, and improved access as their financial profile strengthens over time. Creditworthiness becomes a living profile, not an inherited label.
As continuous scoring becomes more prevalent, the role of alternate data will move beyond origination to inform the entire credit lifecycle: acquisition, pricing, monitoring, restructuring, and retention. Embedded finance will accelerate this trend, as lending decisions are increasingly made at the moment of need, within consumer and business workflows. Consent artefacts enable this fluidity without compromising privacy or control. In this environment, lenders will differentiate not merely by access to data, but by their ability to interpret data contextually, build adaptive risk models, and design credit products that evolve with the borrower.
The future of alternate data, therefore, is defined by the integrity, explainability, and continuity of data usage. Consent-based data sharing ensures the first. Advancements in analytics ensure the second. Continuous scoring ensures the third. Together, they signal a lending ecosystem where credit is personalised, real-time, and fundamentally more equitable.
Conclusion
Consent-based data sharing is redefining how lenders in India evaluate creditworthiness. By enabling secure, standardised, and user-authorised access to alternate data, the Account Aggregator framework and the DPDP Act together create the conditions for more accurate underwriting and broader financial inclusion. Lenders gain a clearer picture of real-time financial behaviour, while borrowers gain control and the ability to demonstrate credibility without prior credit history.
The implications are operational as much as strategic. Faster onboarding, more precise risk assessment, and dynamic portfolio monitoring become achievable at scale. As additional data domains are integrated and continuous scoring models mature, credit decisions will move from static assessments to ongoing, contextual understanding of financial health. The advantage will not come from access to more data, but from the ability to use the right data, responsibly, transparently, and in real time.
FAQs:
1. What is consent-based data sharing in lending?
Consent-based data sharing is a framework in which a borrower explicitly authorises a lender to access specific financial information for a clearly stated purpose and duration. The borrower retains full visibility into what data is being shared, with whom, and for how long. This model ensures that data is shared voluntarily, transparently, and with the ability to withdraw access at any time. In India, the Account Aggregator (AA) framework operationalises this model in a secure and standardised way, enabling lenders to use alternate data responsibly. It shifts data access from “implied permission” to documented, traceable consent.
2. How does the Account Aggregator framework relate to alternate data?
The Account Aggregator (AA) network acts as the secure infrastructure layer that enables alternate data to be shared in a structured, machine-readable format. Prior to AA, alternate data such as bank statements, platform payouts, and GST filings often came through screenshots, PDFs, or unverified uploads, limiting reliability. With AA, lenders can access this data directly from regulated institutions based on borrower approval. This standardisation and authenticity make alternate data suitable for automated credit models and compliant underwriting workflows. Put simply, AA turns alternate data into institution-grade decision data.
3. Why is alternate data important for thin-file or new-to-credit borrowers?
Traditional credit scores rely on past borrowing history, which many borrowers such as gig workers, first-time borrowers, and small merchants, simply do not have. Alternate data reflects current financial behaviour, such as income consistency, spending discipline, savings habits, and repayment patterns. This allows lenders to evaluate ability and intent to repay, even without formal credit history. As a result, alternate data supports greater inclusion without compromising risk assessment. It enables lenders to serve segments historically overlooked by bureau-only scoring models.
4. How does the DPDP Act strengthen trust in alternate data usage?
The Digital Personal Data Protection (DPDP) Act establishes a clear legal foundation for how personal financial data may be requested, processed, and retained. It requires that consent must be informed, specific, unambiguous, and revocable, giving borrowers greater control over their data. Lenders must disclose the purpose of data use and the duration of retention, reducing ambiguity. This regulatory clarity ensures that alternate data is used responsibly, not opportunistically. In practice, DPDP increases confidence for both borrowers and institutions, enabling adoption at scale.
5. Is consent-based data sharing secure?
Yes. Under the AA framework, data is end-to-end encrypted, and Account Aggregators do not store, process, or sell data. They simply act as regulated intermediaries that facilitate secure transmission between financial institutions, based on user consent. Access is governed by a consent artefact, which documents the scope and purpose of sharing and can be withdrawn at any time. This is materially more secure than document uploads or screen scraping, where security and traceability are weaker. The model is designed to ensure that privacy, transparency, and auditability are maintained.
6. How does continuous scoring differ from traditional underwriting?
Traditional underwriting evaluates a borrower’s risk at a single moment typically when a loan is initiated. Continuous scoring, enabled through ongoing consent-based access to financial data, allows lenders to assess financial health in real time. This means lenders can detect early signs of repayment stress, adjust credit limits proactively, and personalise product offers based on current behaviour rather than historical assumptions. For borrowers, improving financial habits can translate into better pricing and higher credit limits over time. Continuous scoring shifts lending from static judgment to dynamic and relationship-based credit assessment.