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For a long time, obtaining credit in India has relied on the traditional system, which involves verifying identification through Know Your Customer (KYC) processes, credit scores, and collateral. This system relies on static information, which is unable to determine how people, such as shopkeepers and labourers, actually earn and spend money today. As a result many individuals, especially those who are new to credit, get left out even after being financially active.
However, in a country like India, in which a large number of people are new to credit, the older system is no longer enough. The limitations aren’t just technical; they are systemic. That’s precisely where India’s Account Aggregator (AA) framework and cash flow-based underwriting come into the picture. Together they underpin a modern, consent-driven, real-time credit decisioning stack.
The biggest bottlenecks here are data fragmentation, manual verification, and lack of context.
The AA framework changes the game. This lets individuals and businesses share financial data, from bank transactions and GST returns to insurance securely while keeping consent in mind on a real-time basis.
What emerges is a layered structure. Financial information providers (FIPs) like banks, mutual funds, and insurers that hold a user’s financial data are at the bottom. At the top sit the financial information users (FIUs), such as lenders, insurers, and wealth advisers, who request access to that data. But the real action happens in the middle layer, where the AA framework itself facilitates user-consented, secure, and real-time data transfer between FIPs and FIUs. This layer is where consent is managed, data is packaged into machine-readable formats, and analytics and decisioning engines come into play. Analytics providers working within this ecosystem build risk, affordability, and eligibility models on top of AA data, often in real-time, using cash flows, income flows, and financial behaviour patterns rather than static bureau scores. Decision engines then interpret those model outputs to generate credit decisions, which are surfaced to lenders and ultimately presented to customers.
Unlike legacy systems, this layer is modular. Different entities can specialise in consent, analytics, or decisioning, and it’s evolving to include feedback loops powered by ongoing user outcomes, enabling constant model refinement.
AA also flips the visibility paradigm: users control when and how their data is shared, and audit trails offer transparency into data flows.
The credit system is advancing. Income is now judged through real-time cash flow and not old static proofs. Risk models are evolving and evaluate financial activities through real-time behaviour and not past data through secure APIs. This shift also makes credit more personalised, moving away from generic products to offerings tailored to individual affordability and cash flow. It’s smarter, faster and more inclusive to assess creditworthiness, and it’s built for today’s borrowers.
What makes cash flow underwriting so powerful is how closely it mirrors real life. Monthly inflows and outflows offer a clear picture of liquidity and repayment capacity. Spikes or dips highlight seasonal income patterns or early signs of financial stress. Everyday transactions like rent, groceries, and EMIs offer meaningful insights into lifestyle and spending behaviour. It allows lenders to assess financial stability with greater context and regularity than a credit score does.
This kind of data is dynamic, behavioural, and consented, allowing for far richer, real-time assessments than a Credit Information Bureau (India) Limited (CIBIL) score. This concept of cash flow underwriting isn’t new. But what has always held it back is scale and standardisation. That’s exactly what the AA system unlocks. This system enables seamless, secure and standardised data sharing. Financial data from FIPs is shared in a uniform format and accessed in real-time through application programming interfaces (APIs), post-user consent. The user stays in control of who sees the data, for how long, and why, all under RBI’s regulatory framework.
This makes it possible to build a cash-flow underwriting engine once and apply it across sectors.
Fair Isaac Corporation (FICO), for decades, shaped credit scores in the US as a proxy for reliability. India now has a chance to lead the next chapter, with cash flow becoming the new credit score and real-time financial behaviour becoming the primary underwriting variable.
This approach is already enabling faster loan disbursals, thanks to the elimination of manual document chasing, along with more inclusive lending even to users without a credit footprint and better risk management via real-time alerts, anomaly detection, and early warning systems.
In a country where over 60 million micro, small, and medium enterprises (MSMEs) exist and only a fraction have access to formal credit, this new decisioning stack is a transformation.
It’s how a first-time borrower in a tier 3 city with stable bank inflows but no prior loans can finally be seen by the system. It’s how a woman gig worker with no formal employment history, but consistent UPI receipts and savings patterns, can be offered a personal loan. It’s how a seasonal trader with GST and bank data can access flexible working capital.
If we do this right, cash flow won’t just be a data point but the foundation of a fairer, more transparent, and truly democratised credit system.
[Disclaimer: The views expressed are solely of the author and Analytics Insight does not necessarily subscribe to it. Analytics Insight shall not be responsible for any damage caused to any person/organization directly or indirectly.]
