2 kinds of consumer Fintech Lending companies

Over the last year building the “AWS for lending” we have partnered with 15+ fintechs across the country. This gave us a unique vantage…

Over the last year building the “AWS for lending” we have partnered with 15+ fintechs across the country. This gave us a unique vantage point in the industry. We had the pleasure to meet many interesting companies and founders to understand a variety of business models in the fintech lending space

So in a world where everybody and their grandma wants to lend, here are the 2 categories of companies which exist in the fintech lending world in India today

Performance marketing ~ DSA Companies

  1. How to identify such companies?

  • They boast huge AUM’s (500cr-1000cr+ annually) and loan origination numbers (10,000cr+)

  • They have traditional Banks/NBFC’s powering loans in the backend

  • They put up minimal skin in the game

  • They claim sub 2% NPA’s

  • The loan tenures will be longer (9 months+) and ticket sizes will be larger

2. These companies essentially act as the top of the funnel for traditional lenders. They filter borrowers and connect them with the appropriate traditional lenders. They are purely performance marketing companies who get paid per loan disbursed. The fee is usually 1%-1.5%. The underwriting and collections are completely managed by the lender

3. It’s a great model for traditional lenders because it’s like having a DSA who puts up some skin in the game, while lending to the exact same customers they have always lent too

4. Customers are happy because the application process has smooth UI/UX compared to traditional lenders

5. Since disbursement experience is yet owned by the traditional lender, it’s not always smooth. In such cases, the customer's anger is directed towards the fintech and its App Store ratings

6. AUM numbers are a vanity metric here and used as a relic from traditional lending businesses. It's like Google claiming they have an e-commerce business and its revenue is equal to the revenue their ads generate for companies like Flipkart, Amazon, etc

7. The lifetime value (LTV) of customers here is low because they can always go directly to traditional lenders to avail the same services

8. The cost of customer acquisition (CAC) is high here because of the high competition to underwrite this customer since he’s already considered creditworthy by traditional lenders

9. No traditional lender is interested in lending basis a startups “proprietary algorithm”. Reasons are simple

  • The business generated is way too small for them to care or bother to change

  • Their systems have standard products built in and they simply do not have the motivation or technological capability to build in new product categories

  • The unit economics of doing new products or small ticket size loans does not make sense due to their overheads. As an experiment, go to your nearest branch and ask them for a Rs 15000 loan for 3 months. No matter what you do, they will not sanction it

Proprietary Lenders ~ IP creating companies

  1. How to identify such companies?

  • They claim huge no. of loans disbursed in a month. These are companies with fairly small AUM’s

  • They will have new age NBFC’s powering loans to their customer in the backend

  • They put up significant skin in the game

  • They claim sub 5% NPA’s

  • The loan tenures will be shorter (6 months and lower) and ticket sizes will be small

2. The reality here is that these fintechs are either

  • Creating new age products or

  • Catering to customers outside the spectrum of traditional lenders.

3. This means either the product they offer is not available via traditional lenders or the borrowers they are lending to would be rejected by traditional lenders.

4. The IP creation force is strong here. The moat here is the credit algorithm, collections and the ops needed to make this truly new business sustainable

IP creating companies take the path less traveled

5. Significant skin in the game and the higher NPA’s indicate the learning curve involved in creating new categories. It is a risk and reward game here. They are risking doing something unique with the possibility of higher NPA’s but if they crack it, the potential is huge

6. Internally, we fondly refer to these companies as “category creating” companies

7. The correct metrics here are the number of loan cycles which have been seen by the fintech and the number of loans processed per month. The higher these metrics the more feedback their credit algorithm has gotten and thus the more it has improved. If you crack a new category, that algorithm becomes the moat

8. The scale-up here has to be once the credit algorithm has seen enough cycles. Enough cycles imply your model has seen statistically significant data and your NPA’s have stabilized to a point which makes sense as per your business model

9. The lifetime value (LTV) is high and cost of customer acquisition (CAC) is low in this case since this category is not catered to by anyone else

10. The idea of these companies to do small ticket size loans, low duration loans so as to improve their algorithm fairly quickly while being capital efficient

So the next time you come across a fintech, do ask yourself which of the 2 segments do they belong to and why. In a world full of PR and flashy numbers being thrown around, it is an interesting exercise to peel back the onion and see what’s actually cooking. Internally, we love “category creating” companies and do our best work while working with them.