A brief study of current fintech startups,thier business models, target segments catered to, underwriting methodology used and product offerings

1. Fintech- Target segment, Business Model & key Player
Indian startup has suddenly caught up with 'Fintech' and 'Lending' Mania . There is sudden influx
of multiple startups trying to save poor souls from loan sharks, informal channel or making finance
accessible to those outside formal channel, like blue collar workers , student population, un-banked
micro entrepreneurs. Some are even proposing to even create new assets class for investors who
hardly earn 6-7% pa. return on their hard earned money as bank FD
Out of curiosity, I just started digging deeper to understand these digital lending focused startup.
Keeping sarcasm, aside there are indeed few of them who are actually doing a pioneering & ground
breaking work and trying to solve problem using tech, AI, ML, etc. I studied around 50 startups
to understand their business model, their target segment, product offerings and pricing. Only those
startup were included which were using digital channel to source, or using new data source to
create new underwriting algorithm. Traditional Universal Banks/NBFC following physical branch
channel/Small finance banks have not been included. I have tried to view these players from the
following parameters.
Business Model variations
1. Act as a market place model for borrower & lender. Here the online portal acts as
intermediary and also does value addition by using their own proprietary underwriting
algorithm. This propitiatory algorithm uses additional data sources over & above what a
bank/NBFC uses in traditional sense, for example, the payment data from PoS, e-commerce
portal, social media data etc. Documents are uploaded and final loan book stands on books
of lender/NBFC , again platform earns money out of fees for each lead provided. Lending
institutions may or may not trust the underwriting model of partners fin-tech firms.
Example-MoneyTap, Finreq, Credit mate
2. P2P Lending platforms- Up in the value chain comes the P2P lending platforms wherein
individual borrower & individual lenders come on platform to enter into loan contract
facilitated and backed by its own underwriting model. The credit risk is borne by the
individual lender, however documentation and collection process is facilitated by P2P
platforms. Platform earns by charging inter mediation as service fees. Example - Faircent,
i-Lend, LenDen Club
3. NBFC with loan on books using proprietary underwriting model- These are the full
fledged NBFC's with a corpus of fund borrowed from others. The have sourcing platform
for customer to register and apply, upload documents and propitiatory underwriting engine
using mix of conventional ,new data sources and AI logic. -Capital Float, LendingKart,
Incred
4. Facilitator- Not necessarily involved in process directly but working in lending process
value chain so to facilitate new ways of risk assesment and bringing newer players into
formal banking channel. Example-Credit vidya, LazyPay
5. Aggregation platform or channel partners on behalf of Banks/NBFC for various retail
segment products( Home loan, personal loan, education loan) or MSME loans. All the loan
documents are uploaded by borrowers on platform which is accessible by lenders.Final
loan stands on books of lender NBFC/Banks and platform earns money in form of fees for
each lead provided to Lending institutions. Final terms & conditions are as decided by

2. lenders. I wouldn't describe them as Fintech firm. They are just a lead funneling website
for traditional lenders. Example-Rubique, Billion loans
Target Segment, products & pricing
Fintech firms are also innovating in terms new target populations being provided loans. Primarily
the target segment is those where the ticket size is small in the range of 2k to upto 10 lacs in retail
segment & upto 1 crore in business loan. The business logic to select target segment is to leverage
technology to reduce customer acquisition and data collection process and scale up fast. Some of
them are like
1. Money Tap/Credy-salaried private sector employees primarily in metro cities.
2. LendingKart/Capital float-Micro & small enterprises not being serviced by universal
banks. Capital float average ticket size is 25 lacs and tier-1 cities focused, while
lendingKart is more distributed, average ticket size 4 lacs and borrowers from across cities.
3. Kredx- Invoice financing of blue chip companies by individual investor.
4. CreditMate- Fresh & used Two wheeler purchasers (lower middle class) of tier1 & tier-2
cities.
5. Zest money/Kissht/Happy EMI- E-commerce & PoS buyers
6. Neogrowth credit- MSME merchants with EDC & Pos Sale with daily account debit for
loan repayment
7. Rupeek- Gold loan with sourcing done vie digital channel
8. Open Tap/SERVD/- Blue collar workers
9. Credit Vidya- Fist time borrower with no credit history.
10. SlicePay/Krazybee- Loans to students to take care of their personal need. (the surprising
one...for me)
The products being offered are traditional working capital facility (mostly Over draft) for short
duration from as low as 7 days to upto 9 months. The business loans and personal loans to salaried
class is for tenure upto 3 years, similar to that of traditional financial institutions. Few of the
startups provide the flexibility of variable EMI or prepayment of loans without charges etc.
The pricing is key, for the plain vanilla lead generators, the pricing is similar to that decided by
partner Banks/NBFCs. For P2P platforms, pricing is mutually decided between borrower & lender,
nevertheless it varies from 12 % p.a. to upto 30% p.a. For the NBFC in direct lending it varies
from 18% to 30% p.a.
Innovation in underwriting process
Coming from traditional banking background, I am definitely curios about the underwriting
framework and risk assessment. The pertinent question that comes to my mind is, how come in
digital lending, wherein the borrowers may not be have even met the lender or maximum one
physical verification might have been carried out on behalf of lender, the money lent is safe. Or
what is the mechanism which can act as deterrent towards default.

3. On account of segment focus, the startups are using new data points not being used by traditional
banks/NBFCs. Traditionally account statement analysis, KYC checkup, ITR , Salary slips,
Financial statement analysis, are checked followed by the bank/NBFC official visit to borrower
office/ Business place and home.
These digital lending startups use additional data points like Aadhar authentication, data
provided by e-commerce portals, data from merchants service providers, social media ,
psychometric analysis using data obtained from application form filling. Data points accessed
through mobile app like SMS analysis, location, types of apps installed, camera access, call logs,
contacts, etc.
Post extraction of these data, underwriting algorithm making use of artificial intelligence and
machine learning is developed. These algorithms are regularly refined and tweaked to get
optimum result. Here startups with early mover advantage will gain more as they have repository
of past data , which can be utilized to build precision algorithm and scale fast.
Though I don't have much data about the NPA at sector level , however in few of the NBFC
platforms , as stated by them, the NPA ratio is in range of 3-5% . The provisioning norms are
also lender specific and few of them do 100% provisioning within 45 days of default.
The digital lending sector is fast evolving, and currently, startup and traditional players are
looking forward to co-operation and creating a win-win case. As the ecosystem matures and
process becomes more and more robust traditional banks/NBFC will start venturing into the
digital lending space. Few have started co-lending model as well. Curiously watching the
space....