Recent estimates by CRIF report that the lending market in India (as of March 2021) grew by 100% to Rs 156.9 lakh crores between FY 2017 and FY 2021. Retail lending portfolio grew by 91%, micro lending by 157%, and commercial lending by 93% during the same period.
Today, India has an estimated 63 million small and medium businesses with continuously growing credit demand. Traditional credit risk assessment models were woven around analyzing a fixed set of variables. However, the overall credit supply from banks and other established financial institutions is struggling to match this demand ~ a gap of 67.3% that isn't served. A new generation of borrowers, NTC customers, and the ever-expanding digital penetration is bringing in massive data sets exponentially.
Adopting data analytics has become paramount for financial institutions to scale lending operations while continuously lowering risks. Banks and Financial Institutions(FI) will have to move beyond existing credit decisioning methodologies which may limit the quality of credit risk analysis. The most pertinent challenges are: (1) extract and analyze huge amounts of data - structured and unstructured, and (2) draw logical and precise interpretation of data before arriving at smarter, instant lending decisions.
Data Science solutions based on AI & ML can empower FIs to stretch the expanse of vetting a customer’s profile across a wide range of data sources, variables, and weightages to arrive at better risk profiling. This will help FIs to take advanced precautionary measures in their credit-offtake decisioning. Lentra is empowering FIs with technology and intelligence beyond traditional bureau reporting mechanisms, through various analytical scorecards.
Lentra’s Scorecards help take powerful data-backed and predictable lending decisions in real-time, infusing agility into credit underwriting teams. Tried and tested with millions of customer data profiles, our highly configurable automated scorecards are helping increase lending approvals by upto 40% at ~96% STP.
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