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© 2024 LightFi India Private Limited. All rights reserved.
(Formerly known as Habile Technologies)
Published August 2023 on LinkedIN
© 2024 LightFi India Private Limited. All rights reserved.
(Formerly known as Habile Technologies)
After smartphone penetration, people are not watching their SMS at all. They use SMS only for OTP related transactions. That’s it.
But What can a Lender see in your SMS after you consent to them?
Lender can see income, expenses, and any other Fixed Obligation like (EMIs/Credit Card).
1) Income – Parameters like Average Salary Credited, Stable Monthly inflows like Rent
2) Expenses – Average monthly debit card transactions, UPI Transactions, Monthly ATM Withdrawal Amount etc
3) Fixed Obligations – Loan payments have been made for the past few months, Credit card transactions.
It also tells the Lender the adverse incidents like
1) Missed Loan payments
2) Cheque bounces
3) Missed Bill Payments like EB, LPG gas bills.
4) POS transaction declines due to insufficient funds.
A massive chunk of data is available in our SMS (more than 700 data points), which helps Lender to make a credit decision.
An interesting insight on vehicle loans for lenders.
A trend we are seeing today – the first-hand vehicle ownership is decreasing with time. Why? People are upgrading their vehicles in every few years because of technological advances. And, this can be seen more with the millennial generation.
So, what should a lender do in terms of financing?
– Estimating the residual value of the vehicle at the start of the financing period.
– Charging a borrower only for the residual value (which is the difference between the value after a few years and the current value)
Example: A bike currently is INR 1 lakh. You want to buy the vehicle for 2 years. A lender will estimate the residual value of that bike today and what it would be after 2 years. If the estimated residual value = INR 45,000, the lender will charge you only that (say, INR 55,000 with interest for this instance) during your tenure.
At the end of 2-year period, you have 3 choices:
1. Return the bike and upgrade to a new one without going through the struggle of selling it.
2. Pay the lump sum remaining amount to own the vehicle outright.
3. Extend the financing and own it by keep paying the EMIs for the remaining amount of the vehicle for the next 12 or 18 months.
Benefits for the borrowers?
– Flexibility to use a vehicle and upgrade to a new one.
– Affordability to not pay for the complete value of the vehicle with the intention to use for a lesser amount of time.
– Convenience in owning the vehicle.
Say goodbye to the old lending option and embrace the new way of financing for vehicle by lenders!
How many of us know this?
1) Tiktok does Lending ( is it an entertainment company or social media company or a fintech company?
2) Youtube China does Lending
3) Top 100 internet companies in China(no matter what business they are in) do Lending
The team which was heading Lending in Tiktok was the Advertisement team. If we do Ads, we do X no of revenue. But if we do lending, we’ll get X+30% more revenue. This is on the same Ad spot.
Ad team has transformed into a lending team, and in today’s world, it’s possible because the subject matter expertise can be put in as an API and given to you.
Embedded Lending as a service is becoming popular in India too, and I am happy to be part of this ecosystem.
The answer is No. Only the top 10 crore people have access to many credit products in India. Almost all Banks focus on this market.
Once you go beyond that, the credit access rate has dropped significantly due to multiple factors.
1) Customers who are having low income(30-40K per month)
2) Not earning from an employer who belongs to Category A or B
3) Not from Tier 1 or 2 cities
NBFCs and Fintechs focus on the above segment, pushing another 10 crores of people.
But in India, 70 crores more people are formally or informally employed, which still needs to be tapped.
These digital lending AI models make decisions based on Geolocation.
According to a study in the US: Lending behaviour was highly correlated to sugar-related diseases in a “PARTICULAR STREET”.
For people who don’t have bureau scores or proper bank statements, Geolocation is considered for underwriting.
What these models will give based on street-level intelligence:
1) Who lives here – are these wealthy people or low-income group people
2) People living in these streets are crowded – if the Geolocation belongs to a slum, then it’s a risky user. (I felt bad after hearing this)
3) What kind of stores are there: For example, More Auto repair shops in a street which says this is from a lower income group area and expensive restaurants signal wealthy people
4) The real estate value of this location
More than 2000 parameters can be analysed with Geolocation.
Based on these data, we can arrive few things.
1) The collection behaviour
2) What financial products can be sold to them
whats ur thoughts on this?
#digitallending #geolocation #manispeaksmoney