$UPST +8% NEW IPO
A lending marketplace that uses machine learning to provide loans
Sales grew 44% in first 9 months of 2020 to $ 164m and recored a profit of $ 4.6m
Despite a LARGE contraction during the pandemic
REASONABLY valued at 15 Price/Sales
A lending marketplace that uses machine learning to provide loans
Sales grew 44% in first 9 months of 2020 to $ 164m and recored a profit of $ 4.6m
Despite a LARGE contraction during the pandemic
REASONABLY valued at 15 Price/Sales
Upstart $UPST is an online lending marketplace that provides personal loans by using machine learning
It was founded in 2012 by Dave Grirouard, former president of Google Enterprise, Paul Gu, a Thiel Fellow and Anna Counselman, a former Google manager https://techcrunch.com/2020/11/05/inside-fintech-startup-upstarts-ipo-filing/
It was founded in 2012 by Dave Grirouard, former president of Google Enterprise, Paul Gu, a Thiel Fellow and Anna Counselman, a former Google manager https://techcrunch.com/2020/11/05/inside-fintech-startup-upstarts-ipo-filing/
It developed a model that is capable of determining the creditworthiness of prospective borrowers
Next to traditional variables (FICO score, income, credit report) it relies on the following features:
Academic variables (GPA, area of study, colleges)
Work history
Next to traditional variables (FICO score, income, credit report) it relies on the following features:
Academic variables (GPA, area of study, colleges)
Work history
Upstart doesn’t make loans itself:
Rather it connects consumers to its network of Upstart AI-enabled bank partners
These consumers benefit from lower interest rates, higher approval rates and a highly automated and digital experience
Rather it connects consumers to its network of Upstart AI-enabled bank partners
These consumers benefit from lower interest rates, higher approval rates and a highly automated and digital experience
In more tangible terms:
· It uses over 1,600 data points to score borrowers
· Upstart provides 27% more approvals than traditional models
· 16% lower rates than traditional models
· It uses over 1,600 data points to score borrowers
· Upstart provides 27% more approvals than traditional models
· 16% lower rates than traditional models
Upstart’s model is built on its “AI” flywheel
It uses more data and better models to undercut competition
Customers get approved and get better interest rates
With more data, Upstart is able to generate more loans
It uses more data and better models to undercut competition
Customers get approved and get better interest rates
With more data, Upstart is able to generate more loans
It is then able to improve its models, cut down its loss ratio and select good borrower more efficiently
Upstart’s loans go from $ 1,000 to $ 50,000 and concern 3 & 5 years loan terms with an APR range of 8.27% to 35.99%
99% of these loans are funded within 1 business day after signing
Borrowers can pay their loan back early at no cost
99% of these loans are funded within 1 business day after signing
Borrowers can pay their loan back early at no cost
What about the customer base?
Upstart is dedicated to personal loans, it thus offers helps customer with:
Moving loans
Home improvement loans
Medical loans
Credit card consolidation
Debt consolidation
Wedding loans
Upstart is dedicated to personal loans, it thus offers helps customer with:
Moving loans
Home improvement loans
Medical loans
Credit card consolidation
Debt consolidation
Wedding loans
How does Upstart makes loans?
Directly through the Upstart website
· A partner bank emits the loan as Upstart itself doesn’t provide any loan
· The loan can be retained by the bank, sold to Upstart’s network of institutional investors or funded by Upstart’s balance sheet
Directly through the Upstart website
· A partner bank emits the loan as Upstart itself doesn’t provide any loan
· The loan can be retained by the bank, sold to Upstart’s network of institutional investors or funded by Upstart’s balance sheet
· In Q3 ’20, around 22% of the loans issued directly through Upstart were retained by the originating bank
· Around 76% were purchased by institutional investors (such as PIMCO, Goldman Sachs, Morgan Stanley)
· Around 2% were funded by Upstart’s own balance sheet
· Around 76% were purchased by institutional investors (such as PIMCO, Goldman Sachs, Morgan Stanley)
· Around 2% were funded by Upstart’s own balance sheet
Through a white-label product on the bank partner’s own website
· The bank uses Upstart’s models and algorithms to predict a prospective borrower’s creditworthiness
· It then makes the loan on its own
· The bank uses Upstart’s models and algorithms to predict a prospective borrower’s creditworthiness
· It then makes the loan on its own
What is the business model?
Upstart generates the majority of its sales from fees paid by banks:
Referral fees for each loan referred through Upstart
Platform fees for each loan originated
Loan servicing fees as customers repay their loans
Upstart generates the majority of its sales from fees paid by banks:
Referral fees for each loan referred through Upstart
Platform fees for each loan originated
Loan servicing fees as customers repay their loans
Great!
Upstart was founded by a team of ex-Silicon Valley employees that wanted to apply their experience in machine learning to loans
It choose the personal loans market and makes money by collecting fees from banks
But how large is their market?
Upstart was founded by a team of ex-Silicon Valley employees that wanted to apply their experience in machine learning to loans
It choose the personal loans market and makes money by collecting fees from banks
But how large is their market?
According to TransUnion, there were $ 118B in unsecured loans from April 2019 to March 2020 - growing at 8% YoY
Upstart facilitated the origination of $ 3.5B in unsecured personal loans, accounting for 5% of the total market
Upstart facilitated the origination of $ 3.5B in unsecured personal loans, accounting for 5% of the total market
According to Mordor Intelligence, the digital lending market is expected to grow at a CAGR of 11.9% over the 2020 - 2025 period
Driven by the proliferation of smartphones, advanced in machine learning and cloud computing
Driven by the proliferation of smartphones, advanced in machine learning and cloud computing
“Also, technologies like Artificial Intelligence, Machine Learning, and Cloud Computing benefit the banks and fintech as they can process huge amounts of information about customers.” - Mordor Intelligence
https://www.mordorintelligence.com/industry-reports/digital-lending-market
https://www.mordorintelligence.com/industry-reports/digital-lending-market
Upstart’s ambitions do not stop at personal loans
“[…] by applying our AI models and technology to adjacent opportunities, we believe we are well-positioned to address the U.S. auto loan, credit card and mortgage markets.” - Upstart S1
“[…] by applying our AI models and technology to adjacent opportunities, we believe we are well-positioned to address the U.S. auto loan, credit card and mortgage markets.” - Upstart S1
According to the Federal Reserve Bank of St Louis, from April 2019 to March 2020, there were:
· $625 billion in U.S. auto loan originations
· $363 billion in U.S. credit card originations
· $2.5 trillion in U.S. mortgage originations
· $625 billion in U.S. auto loan originations
· $363 billion in U.S. credit card originations
· $2.5 trillion in U.S. mortgage originations
“In June 2020, we began offering auto loans on our platform, and in September 2020, the first auto loan was originated through the Upstart platform” - Upstart S1
Things look bright for Upstart!
The company developed a model that relies on non-traditional data to predict a borrower’s creditworthiness
It started with the personal loans segment and already serves 5% of the market
The company developed a model that relies on non-traditional data to predict a borrower’s creditworthiness
It started with the personal loans segment and already serves 5% of the market
But it also plans to enter the auto and mortgage market, unlocking a massive TAM
As for most data-driven players, their flywheel effect is built on increasing the data they can access by increasing their customer base
But is this profitable?
As for most data-driven players, their flywheel effect is built on increasing the data they can access by increasing their customer base
But is this profitable?
Financials Check
Sales grew 44% in first 9 months of 2020 to $ 164m
Spent $ 80m on borrower acquisition, verification and servicing costs
Recorded a contribution profit of $ 63m in first 9 months of 2020 up 151% year over year
Sales grew 44% in first 9 months of 2020 to $ 164m
Spent $ 80m on borrower acquisition, verification and servicing costs
Recorded a contribution profit of $ 63m in first 9 months of 2020 up 151% year over year
Despite a large contraction in lending during the pandemic as its partners paused lending
Operating expenses reach $ 145m up 32% from $ 110m a year earlier
It recored a profit of $ 4.6m in the first 9 months of 2020 up from a loss of $ 10m a year earlier
Operating expenses reach $ 145m up 32% from $ 110m a year earlier
It recored a profit of $ 4.6m in the first 9 months of 2020 up from a loss of $ 10m a year earlier
THE BOTTOM LINE
$UPST is a fast growing digital lending player that uses machine learning to predict borrows creditworthiness
It is able to provide 27% more approvals that traditional players and 70% of its operations are automated
$UPST is a fast growing digital lending player that uses machine learning to predict borrows creditworthiness
It is able to provide 27% more approvals that traditional players and 70% of its operations are automated
It started with the personal loans market which grew 8% year over year and plans to enter the auto and mortgage market
Upstart doesn’t hold the risks related to lending as it simply refers customers to loan providers
Upstart doesn’t hold the risks related to lending as it simply refers customers to loan providers
Given its TAM, management and financial metrics, it is reasonably valued at 15 PS
The lending market is strongly correlated to the business cycle
A rise in interest rates might negatively influence Upstart’s growth
We take a full stake into Upstart
The lending market is strongly correlated to the business cycle
A rise in interest rates might negatively influence Upstart’s growth
We take a full stake into Upstart
$DHER.DE is on our watchlist To Be Reviewed SOON
Disclaimer - This is not investment advice in any form and investors are responsible for conducting their own research before investing.
Sources
✑ Investor presentation
✑ Company website
✑ Mordor Intelligence
Disclaimer - This is not investment advice in any form and investors are responsible for conducting their own research before investing.
Sources
✑ Investor presentation
✑ Company website
✑ Mordor Intelligence
✑ TransUnion
✑ FED
✑ TechCrunch
✑ Crunchbase
✑ FED
✑ TechCrunch
✑ Crunchbase
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