The changing dynamics of the lending industry makes it pertinent to understand the nuances of credit behavior with the use of alternative data and not relying upon credit information alone. With 1/3rd of the demand for credit coming from the New to Credit (NTC) borrower profiles, it is extremely relevant & crucial to use alternative data analytics to assess more MSME profiles. TransUnion CIBIL, in its endeavor to help the lending industry take better informed decisions and remove information asymmetry, has partnered with Online PSB Loans Limited (OPL) to provide an objective risk assessment tool called FIT Rank which uses alternative data sources - Financial, Income & Trade data.
FIT Rank makes use of:
- Banking information through bank statements
- Financial & Income Information from Income Tax Returns
- Trade data through GST Returns
How does FIT Rank work?
FIT Rank triangulates Financial, Income & Trade information from multiple sources such as: Banking information through bank statements, Financial & Income Information from Income Tax Returns and Trade data through GST Returns & uses machine learning algorithms to predict the probability of a borrower defaulting on its loan repayment in the next 12 months.
FIT Rank risk differentiates on a scale of 1 to 10, with FIT-1 being the least risky & FIT-10 being the most risky borrower.