Digital Lending and Savings Platform


The client is a Fintech startup which provides loans to grey-collar employees through its app. While the founders had a banking background, the technology & data science expertise was provided by Quantilus.


While everyone faces financial emergencies at one point or the other, formal credit systems such as banks and NBFCs are not eager or readily available to give loans to people from the lower middle class and neglected sections of society in India – such as housekeepers, fruit / vegetable vendors, etc. 

The interest rate charged by informal channels has no limit and people’s perils are distressing. Borrowers are led into a vicious cycle of loans. The founders realised that the Next Billion in India was completely underserved by traditional, organised financial firms. They needed a streamlined and transparent way to borrow credit. The client hit upon the idea of harnessing the power of mobile phones, enabling ‘credit to all’ option. 



Quantilus helped the client in building India’s next-gen digital lending and savings platform. Our team developed unique data science models to democratize credit for millions of borrowers. Some of the features of the app include: 

  • Real-time analytics and credit reports based on alternative data enable the client to reach unserved and underserved market segments.  
  • The app makes fair and transparent credit available to people to provide meaningful financial inclusion. 
  • The app has further strengthened its financial inclusion goal through its financial wellness program, for corporates and individuals.  
  • The app also includes mini and microloans, insurance, Bill Payments, mutual fund SIPS, E-Gold and e-Tax filing service.  
  • Apart from English, the app also provides support for customers in regional languages such as Kannada, Tamil, Hindi, Telugu, and Marathi. 


Traditional credit reporting agencies often rely on credit history and financial records, making it difficult for underserved segments of society, such as housekeepers, vendors, and others who may not have traditional credit records, to access financial services. To address this gap, Quantilus data scientists used alternative data sources and machine learning models to develop credit reports for underserved populations. 


Quantilus used utility bill payment history, mobile phone usage, and other non-financial data sources to build a more comprehensive profile of an individual’s creditworthiness. We also built models to analyze transaction data from mobile payment services used by housekeepers and vendors to identify trends in their income, spending, and savings behavior. 


By leveraging these alternative data sources and applying machine learning models to the data, our data scientists created credit reports that are more comprehensive, accurate, and inclusive of underserved populations. 

The client has raised USD 27.7 Million in 6 rounds of funding, and now aims to become a full-stack Digital Bank by 2026. 



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