In the evolving landscape of global lending, traditional credit assessment methods need further augmentation to meet the demands of speed, inclusion, and accuracy. Traditionally, lenders relied heavily on bureau data such as credit scores, repayment histories, and outstanding liabilities to make underwriting decisions. This approach works by using retrospective analysis of the credit behaviour, often leaving out large segments of the population which do not have enough credit history.
This need has led to a new approach often referred to as precision underwriting. Precision underwriting is a modern approach where decisions are based on deeper analytics of individual behaviour, real-time data and the use of AI/ML models for risk prediction. This is powered by combining bureau data with real-time cash flow insights. This shift is changing how lenders assess risk, expand access, and compete in digital financial ecosystems.
The shift to precision underwriting
The advent of precision analytics is evident globally, with more than 78 countries, as of early 2025, having some form of open banking or open finance framework in place, enabling secure and consent-driven access to financial data. At the same time, nearly 85 per cent of Tier1-banks globally have already incorporated transaction-level or cash flow data into their processes. This is not just a trend driven by technology, but by stronger risk management and lower risk tolerance.
As per various articles, a few institutions have declared that use of alternative data has been shown to improve approval rates by 15 to 25 per cent without increasing default risk, while combining bureau and cash flow-based early warning signals can reduce default rates by 10 to 20 per cent and significantly improve model accuracy. These gains are especially important when considering that around 1.4 billion adults globally remain underbanked or lack sufficient history, making them difficult to assess using traditional methods alone.
India’s moment in credit innovation
India presents one of the most compelling case studies of this shift at scale. We have over 800 million internet users and a rapidly growing digital payments ecosystem driven by , which processes billions of transactions every month. Despite this, a large portion of the population remains new to credit, with an estimated 30 to 40 per cent of borrowers having thin or no bureau history.
The rollout of the Account Aggregator framework has enabled consent-based sharing of financial data across banks, NBFCs, and fintechs, creating a foundation for cash flow-based underwriting. At the same time, India has more than 60 million MSMEs, many of which lack formal credit access despite having active bank accounts and transaction trails.
Early adoption of cash flow-based models in India has shown meaningful improvements, including higher approval rates for new-to-credit users and better risk differentiation for small businesses. This combination of scale, digital infrastructure, and regulatory support makes India a leading market for precision underwriting innovation.
Combining bureau and cash flow data
When bureau data and cash flow data are combined, the result is a more complete and balanced understanding of risk. Bureau data provides insight into long-term financial discipline, while cash flow data reflects current affordability and resilience. To support this approach, lenders are building modern data architectures that can ingest, process, and analyse multiple data streams in near real time. These systems pull in bureau records alongside transaction-level data from banks and other financial sources, then clean and categorise that data into meaningful signals such as income, essential expenses, and discretionary spending.
Advanced models transform these signals into risk indicators, including income stability, expense ratios, and liquidity buffers. These indicators are then used within decision engines that combine statistical models with policy rules to generate more precise credit decisions. Importantly, this process does not end at loan approval. Continuous monitoring allows lenders to track changes in borrower behaviour and respond early to signs of stress, making underwriting an ongoing process rather than a one-time event.
Expanding access with better risk models
The strategic advantage of this model is very evident in the fact that lenders can make more accurate decisions by reducing both over-leverage and under-. By deploying these models, lenders can extend credit to segments that were previously excluded, such as gig workers, micro and small businesses, and individuals who are new to formal credit systems. This expedites the decision-making process and reduces the underwriting time from days to minutes, thus improving customer experience and operational efficiency.
At the same time, lenders gain the agility to make risk management decisions dynamically, such as credit limit adjustments and risk-based pricing on real-time risk signals, creating more adaptive risk management practices leading to a resilient lending portfolio. However, designing next-generation risk models requires integration of these data sources, which are structured and standardised. This ensures that models produce reliable results. The AA ecosystem also takes care of the data privacy and consent requirements where customers have control over their data.Having said that, lenders need strong systems to handle data integration and processing.
The future of smarter lending
In the future, this combined approach will become standard. Lenders will rely on both historical and real-time data to make decisions.The power of bureau and account aggregator synergy lies in its ability to provide a complete view of the borrower. This leads to better decisions, improved inclusion, and stronger risk management.
Disclaimer: The information provided in this article is for informational purposes only and does not constitute financial, legal, or professional advice. While every effort has been made to ensure accuracy, readers should verify details independently and consult relevant professionals before making financial decisions. The views expressed are based on current industry trends and regulatory frameworks, which may change over time. Neither the author nor the publisher is responsible for any decisions based on this content.
Sachin Seth,Regional Managing Director, CRIF India & South Asia