M Rajeshwar Rao, Deputy Governor, Reserve Bank of India (RBI)
| Photo Credit:
Abhijith
The time may not be far when alternative data such as cash flow information will no longer be alternate, but will be the mainstream in the national financial architecture, according to M Rajeshwar Rao, Deputy Governor, Reserve Bank of India.
While Credit Information Companies (CICs) have undoubtedly played an important role in reducing the information asymmetry thereby facilitating better credit decisions by lenders, they are not going to be the only game in town to source the required data, as information asymmetries are also sought to be addressed through other complementary mechanisms, he said at TransUnion CIBIL’s Credit Conference in Mumbai.
“This trend is driven by the digitalisation of financial services and electronification of records which has created a large repository of data which can be used to get better handle on economic trends, both micro and macro.
“This coupled with the growth of FinTechs and innovations in financial services, has created business opportunities to harness alternate data sets in order to gain a better understanding of financial behaviour and credit worthiness of individuals and entities,” Rao said.
He observed that these insights can give a richer perspective than conventional analysis and provide an impetus to the measures taken to foster greater financial inclusion.
According to a PwC report, alternative data sources include income proxy, spend pattern, risk and compliance, geography internet of things (IoT), customer behaviour, aggregators and thought leadership, and Government data.
Role of AI/ ML in facilitating credit delivery
The Deputy Governor underscored that one of the main challenges in the provision of credit facility, especially among underprivileged populations, is the absence of credit history.
By using artificial intelligence (AI) and machine learning (ML), algorithms can evaluate alternative data from diverse sources to determine creditworthiness more accurately, he added.
“In fact, it seems that time is not far when alternative data will no longer be alternate, but it will be the mainstream. This advancement would allow lenders to extend credit to individuals who were once deemed ineligible.
“Use of AI/ML could simplify the disbursement process by automating credit assessments and risk evaluations, which not only accelerates fund distribution but also cuts administrative costs, making it practical to offer small loans even in remote regions,” Rao said.
Moreover, AI models excel at uncovering previously hidden insights in data, enabling financial institutions to more precisely forecast their clients’ funding requirements and creditworthiness.
They also streamline compliance workflows, such as Know Your Customer procedures, which significantly cuts operational costs and increases their speed of lending.
Rao said microfinance and microloans which serve as crucial support systems for underserved communities are likely to be the biggest beneficiary of this advancement.
However, he cautioned that the use of complex AI and ML models introduces concerns around model risk, especially when these models are not thoroughly tested, validated, or monitored for biases and performance drifts.
Rigorous validation protocols, continuous monitoring, and robust governance frameworks are essential to ensure that these models remain fair, transparent, and aligned with regulatory and ethical standards.
ULI: Harnessing e-com platform data
Rao emphasised that one of ULI’s (unified lending interface) standout features is its ability to tap into alternative digital data, enabling access to credit even for those without formal financial histories.
“Its integration with NABARD’s e-KCC (Kisan Credit Card) portal is expected to extend access to customers of District Central Co-operative and Regional Rural Banks, previously excluded from formal digital channels.
“Integration of state-level digitised data, such as land records and cooperative databases into the ULI framework, would provide novel cash flow-based lending solutions,” he said.
Going forward, the potential for ULI to also harness data from e-commerce platforms and gig economy apps could open new doors for credit inclusion for small sellers, delivery workers, and freelancers.
Published on July 2, 2025
