WEBINAR: Machine Learning Insights - Digital MSME Lending, a disruptive game changer

ARX Administrator    Shalabh Singhal CFA, Shreenivas Kunte CFA, CIPM
01 Feb 2019
Category: FinTech / RegTech

Country or region: Asia Pacific (Overall)

Summary:
This webinar qualifies for 1 CE under the guidelines of the CFA Institute Continuing Education Program. 
We encourage CFA Institute members to login to the CE tracking tool to self-document these credits. 


The webinar will address the following learning outcomes. Understanding:

1.  Size of the Digital MSME Lending opportunity in India and its impact on overall economy
2.   Key enablers for the Digital MSME Lending ecosystem
3.   Key Machine Learning applications (credit decisioning and beyond) in Digital MSME Lending
4.   Popular Machine Learning models in Digital MSME Lending
5.   Key risks and control measures in ML powered Digital MSME Lending
 


Abstract

India is home to about 55-60 million MSMEs (Micro, Small and Medium Enterprises). MSMEs play a lead role in generating employment and contributing to India’s GDP. But MSME’s lack access to formal credit. As a result, many of these companies operate well below their potential.

Banks and other financial institutions often deploy manual processes for sourcing, underwriting and servicing credit. These firms are therefore unable to service the credit demand that MSMEs have. Reports estimate that the MSME credit demand-supply gap to be about USD 350 Bn.

The proliferation of cashless transactions, GST and Smartphone penetration have built an alternate data trail for the otherwise thin-disclosure/filed MSME sector.

In this webinar, Shalabh Singhal, CFA, Co-Founder, ZipLoan will discuss how Digital MSME Lenders are using alternate data and ML(machine learning) to solve the “low credit supply to MSMEs” problem that India has.

This is the archived version of a live webinar that took place on 31 January 2018



Date of original publication:

01/31/2019


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Reader Comments

Chirayush Patel
Mar 02 2019
Excellent presentation


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