Fintech

World scenario: Financial Technology (fintech) is relatively new, bringing in disruptive business models in the financial services domain that are challenging the old order of things, shaking its very foundation. LendingClub, the first peer-to-peer lending company founded in 2006, went on to a successful IPO in 2014 and its current valuation stands at 6.5 billion USD . SoFi, a marketplace lender that focuses on providing student loan refinancing, founded in 2011, is close to launching its IPO with a 3.5 billion USD valuation . Globally, fintech investments have grown exponentially in the last year, and the trend is likely to continue. Fintech is not just a buzz. It is real. Looking at Fintech from a global social perspective is also very interesting. In developing countries, the traditional banking infrastructure does not reach a large percentage of the population. Fintech provides a great set of tools to help such economies; they don’t have the barrier of legacy technologies that can restrict developed countries, and are focused on providing low-cost mobile payment solutions to low-income, isolated populations where there is a need for these solutions. Research from Gartner indicates that Africa’s transaction value is forecast to reach 160 billion USD in 2016, with Asia Pacific not lagging far behind. Fintech, therefore, can play an important role in strengthening relatively weak economies by making financial services accessible.

India Scenario: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 such as Affirm, Prospr, LendingClub & Wonga, 11 semi-unicorns (>$500 million valuation) such as Kreditech & Kabbage while the equivalent numbers for paymenttech 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 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.

There are over 11,582 NBFCs and about hundred commercial banks in India, 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% 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: 1. Close to 80% of India does not have a credit score. 2. Credit scores are calculated in a semi-linear manner and are only a rudimentary predictor of the credit risk associated with a borrower. 3. 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.