When we talk about digital-first rather digital-only strategy, data is at the core.
The more the data the better;
the cleaner the data, the better;
the recent the recent the data, the better;
and the usable the data, the better.
GST Data fits the bill quite well. Hence, no wonder it is becoming popular amongst financial institutions for customer on-boarding, credit evaluation, and underwriting. The non-consent data or the publicly available data of the taxpayers (i.e. a GST registered entity) provides reasonable indicators about the profile and health of the taxpayer.
If you are using IRIS Peridot (via app, or APIs), you are already familiar with the details available as non-consent data and the GST return filing history of the entity. Do check out the new version of Peridot app, if you have not already. Many utilities such as voice search, marking your favorite GSTINs etc. is now available. All you need to do is sign up.
Obtaining GST Data for Lending Purpose
The GST data filed in GST returns is quite granular and hence relevant for the lending use cases. Of the various returns filed; GSTR 1 (for sales), GSTR 2A/ GSTR 2B (for purchases), and GSTR 3B (for monthly summary and tax settlement) consist of invoice level and aggregate information that can be used to build credit and underwriting models.
To get access to the GST Returns data, the borrower needs to give consent. To get the data directly from the GST system, the consent is provided via OTP-based authentication. While you can always obtain the data from the borrower using other means such as PDF or spreadsheets, there is a risk of tampering with the data.
Hence, the more authentic source is getting the data right from the Government system. Getting OTP or checking if the borrower has enabled the fetch via API could add interruptions in the digital lending flow. It needs to be addressed by educating the borrowers and a guided user experience to complete the authentication process.
Building Intelligence in GST Data Analysis
Once you have the borrower consent to use GST data for lending and benchmarking your models, there are some aspects of GST data which one needs to be mindful of to get a clear, realistic and accurate picture.
These are the 4 factors that you need to be mindful of while building Credit Models using GST data- consented as well as otherwise.
GST Compliance can be Rewarding
By and large, banks and financial institutions have been targeting large corporates and organisations with a decent ticket-size for funding. Now that an alternative source of ready to consume data is available, the market untapped so far opens up. Using technology to do initial screening and evaluation, reaching out to small entities and proprietors is now cost effective and also scalable.
Fintechs and Banks are exploring the possibilities with GST data and some implementations can already be seen in the lending eco-system.
For the success of GST Data based lending, borrowers (aka taxpayers) also need to be regular and accurate with their compliances. Government has been updating rules so as to make compliance easy for small businesses. And so are solution providers. IRIS Peridot App is also getting upgraded to assist small businesses meet their compliance needs.
Timely and accurate GST compliance is a win-win situation to all participants in the lending eco-system.
Jump-start your digital lending with IRIS Credixo
IRIS Credixo stands at the intersection of Data and Lending Experience. It allows lenders to grow volumes, stand out from the competition and gather meaningful customer data to provide seamless “lending in the box” credit experience and design tailored customer journeys.
Credixo offers set of APIs covering publicly available GST data and consent based data filed in GST returns. Additionally pre-processed data sets, reports and insights are also available as enhanced data APIs which can quickly plug into any system. Banks, lending institutions and fin-techs can integrate these APIs in their credit models and provide faster and seamless lending experience to the MSMEs. Build credit models using GST data with help of IRIS Credixo.