This piece of mine was published on iamWire on 5th January, 2016 here.
2016: The year of FinTech?
Ashwini Anand, CFA
Has Financial Technology or FinTech finally come of age in India? While, FinTech accounted only for 13.3% of the $ 9 billion raised by Indian startups in 2015[i], the gross investment into it was up 8 times over the equivalent 2014 number of $145.1 million[ii]. While that looks amazing at first glance, it has to be noted that m-commerce platform One97 (Paytm) accounted for about 74% of the money raised by FinTech companies this year. Nonetheless, even if one were to exclude this number, Indian FinTech investment has doubled over the 2014 number. India’s FinTech sector has been a far cry from its global peers, accounting only for 6% of the total global FinTech space[iii] (by funding), but clearly as the numbers above show, that is changing fairly rapidly.
FinTech is more than just payment-technology
FinTech in India has been synonymous with payment-technology, a niche that has produced India’s only FinTech unicorn – One97 (valued at $2 billion). However, from a global perspective, FinTech is not limited to payments alone. Globally, there are 11 FinTech lending unicorns[iv] such as Affirm, Prospr, LendingClub & Wonga, 11 semi-unicorns (>$500 million valuation) such as Kreditech & Kabbage while the equivalent numbers for payment-tech startups are 11 and 6. The rest of the FinTech unicorns and semi-unicorns are from a variety of other sub-sectors such as investing, insurance, credit reporting, bitcoin-tech and others. Therefore, among sub-sectors in FinTech, lending is clearly the elephant in the room, followed closely by payments.
India is warming up to core-FinTech
India is warming up to core-FinTech as investors and entrepreneurs being to realize the scale and scope of the opportunity in these sub-spaces such as lending and to a much lesser extent-personal finance. It is not that the scope for payments-tech is saturated, it is just that there are far too many low-hanging fruits in the lending and personal finance space to ignore anymore.
The case for FinTech in lending
There are over 11,582 NBFCs and about hundred commercial banks in India[v] , most of which make limited use of technology in their lending process. Lending decisions are made by credit managers, with limited standardization across applications & varying levels of credit risk management at each institution. $121 billion in individual credit and several times that number in credit to MSMEs is handled this way. The results are nothing to cheer about. Going by data published by the Reserve Bank of India, about 8-9% [vi] of the loans made to individuals go bad when measured in terms of the Impaired Assets Ratio (Gross Non-Performing assets + Restructured assets / Total Advances). This number worse when it comes to loans made to Micro and Small enterprises (MSMEs) at 10-14% depending on the stage in the credit-cycle.
The use of technology is typically confined to the use of a “credit-score” which is calculated based on the credit history of a borrower. The effectiveness of credit score-based lending is seriously undermined by the following:
- Close to 80% of India does not have a credit score.
- Credit scores are calculated in a semi-linear manner and are only a rudimentary predictor of the credit risk associated with a borrower.
- A large number of potential borrowers have limited credit-history and therefore, are plagued by what is known as the “thin-file” problem.
FinTech has been able to solve these problems fairly effectively in developed markets. It has been proven that machine learning algorithms, when applied to existing data available with lending institutions have been able to make better credit decisions than humans and thus improve the profitability of lenders. Cases in point- Kreditech, Lenddo and Kabbage have extremely low default rates in their loan portfolios. In fact, our own software at Monsoon FinTech has been able to improve the on-paper profitability of a large real-world US based loan book by over 58% without any additional information on borrowers by identifying defaulters before they can default.
Further, there are several alternative data points such as social media footprints, call-records and shopping histories associated with borrowers that have been proven to be good predictors of the credit-worthiness of borrowers- a fact widely acknowledged by the banking community & demonstrated time and again by studies.
The scenario in India
There are a limited number of companies in India that are pursuing this space and even fewer which have proven their credit-hypothesis on real world data. However, it has been done and given the size of the opportunity, it is a matter of time before serious money starts flowing into this sector. Capital Float and LendingKart are startups that are pursuing the MSME segment and seem to be gaining traction. Several more will likely join their ranks over time as we have seen in developed markets, with over 10 large FinTech lenders and an equal number of FinTech companies in this space that offer their credit-underwriting software as a service (e.g. Zest Finance, Lenddo, Dataminr, FinGenius).
The big banks know that FinTech is the future and that it is a matter of time before the “alternate lenders” catch up with the established ones. Canaan Partners, which made 50 times its investment in LendingClub seems to think it could happen over the next few years (hit $200 billion)[vii] . 2015 has clearly been a great year for FinTech. Let’s see how much better 2016 turns out to be.
About the author
Ashwini Anand, CFA was with Merrill Lynch, Barclays Capital and Bank of America before starting his first FinTech startup Investopresto that received critical acclaim and close to half a million dollars in funding before shutting down in 2013. His current startup-Monsoon FinTech is trying to disrupt the lending space in India through the application of advanced machine learning technology on unstructured and new-age data for credit-underwriting, to improve the profitability of lenders.