Banks said it couldn’t be done. But innovative entrepreneurs are capitalizing on social media, Big Data and machine learning technology to make capital available to people who couldn’t get it before or couldn’t get it at affordable rates.
One company enables middle-class consumers in emerging markets to gain access to short-term loans by using social media to prove their credit worthiness. Another has, in just 7 years, made $1 billion in business loans to small businesses with poor credit. A third manages a credit marketplace of borrowers and investors in order to facilitate personal and business loans at lower rates than borrowers can get from banks – $7 billion in loans in 7 years. The fourth provides micro-loans without collateral to low-income Hispanic families who lack a credit history.
The companies are Lenddo, OnDeck, Lending Club and Progreso Financiero, and their founders told their stories at the Data Driven NYC Meetup in May.
According to James Gutierrez, Founder & CEO, Insikt, Inc., a financial data analytics company, and formerly CEO of Progreso Financiero, which he founded:
- New regulations for banks have changed lending:
o Credit Card Act
o Dodd Frank
o Basel II
o Basel II/II.5.
- The availability of revolving credit is down – affects small business lending by banks – non-prime consumers are hit hardest
- Technology is changing lending across the value chain, driving the price down
o Applications drive higher volume and lower costs
o Underwriting – big data makes more sources accessible and results in lower risk and increased capital
o Servicing – electronic payments – ACH means lower costs and lower risks
o Mobile payments by lower income borrowers – lenders use SMS for collection to lower cost and lower risk.
- Banks can’t keep up with nonbank alternative lenders, which are transforming all loan products.
Four Nonbank Alternative Lenders Speak
Jeff Stewart, Founder & CEO, Lenddo
- Launched early 2011
- Serves middle class consumers in emerging markets
- Goal was to involve the crowd in lending to the middle class using micro finance techniques and social media data to establish creditworthiness where none exists
- Social data add value by making it possible to map good vs. bad borrowers in terms of affiliation because “birds of a feather gather together” – “even two degrees out tells us how you will perform”
- Integrates social networks with mobile & the cloud to use data sources
- Works with the community on both demand generation and collections/repayments
- Storage was a major technology issue – Chose MongDB at the outset with Amazon Web Services – database grew explosively – all opt in
- User data = social data – grew exponentially – expensive even when only 20K members.
- Realized “It’s big data, not big database” so they moved data to simple storage, created cache MongoDB for queries and cut costs 70% – they think about data use cases.
- No database frees you to solve problems
- Looking closely at bitcoin block chains to add value for transactions.
Noah Breslow, CEO, OnDeck
- Has made $1 billion in business loans to small businesses with poor credit over 7 years
- Loan size is small – $100K-$300K; banks need larger loans or they lose money – they need $1 million and up
- SMEs represent a large and underserved addressable market
- Built a platform to connect Main St. to Wall St., with OnDeck playing all four essential roles: originator, servicer, credit bureau for collecting and aggregating data, and credit scoring (FIC0)
- The database tracks small businesses from birth to death.
- The digital footprint of different stores is totally different and depends on different data sources
- Co. adds private performance data to public data and does a lot of fraud management and triangulation
- Developed a different kind of credit score – it’s a business credit score, not a personal one with the focus on debt service calculations: cash flow, trade credit, business attributes. Social data is noisy – need to look at patterns.
- A large number of small transactions – restaurants, retailers, doctors & dentists, small manufacturers, etc.
- Gather data for scoring a business from many sources. Building a data aggregation and learning platform that includes Mechanical Turk and common sense.
- They price to risk.
Renaud Laplanche, Founder & CEO, Lending Club
- Has created a credit marketplace of borrowers and investors, where Lending Club facilitates loans but is not involved on the credit side.
- As a result, LC can operate at a lower cost than banks – banks have 5%-7% of amounts lent in operating expense vs. LC , which is under 2% and declining.
- LC incurs none of some bank costs, such as the cost of branch offices, reserve requirements, and has lower costs or more advanced technology for customer acquisition, underwriting, origination and servicing.
- Bank’s intermediation cost for credit cards is 16.99%. LC’s range is 7.9%-127% with average intermediation cost of 4.83%.
- A lower lending rate means a higher return to investors.
- Have consistently controlled LC’s growth. Now have 550 employees; hire 100 people every 6 mos.
- Use data for marketing, credit, fraud and collections – receive about 9,000 loan applications a day. Less than 10 are fraudulent, so that fraud becomes a needle in a haystack.
- Fraud predictors: time of day, frequency, etc. New data sources: device, online footprints, application use. Look at consistency of the information provided, behavior online footprint, machine/device and location signal.
- LC uses machine learning to assess risk and predict fraud based on more than a thousand attributes
- Fraud attempts have declined from 5% to 2%.
- Just formed a partnership with Union Bank in San Francisco, which overcomes the challenge of complying with 49 sets of state regulations for Lending Club and opens the way for a traditional bank to offer products it could not otherwise offer. (Probably an indicator of a future trend.)
James Gutierrez, Founder & former CEO, Progreso Financiero
- James was a 2005 MBA from Stanford
- Micro finance gave him the idea for Progreso Financiero – unsecured micro loans and debit cards – delivered from a table in the supermarket – to help immigrants with no FICO scores
- Typical loan size was $1,000 for 12 months. Had to make 10,000 loans to get $1,000 back.
- Immigrants with no FICO scores are a challenging population to underwrite
- What he did:
o Took an eHarmony approach with a robust application with extremely detailed personal data. Detailed data turned out to be valuable.
o Booked some bad loans – a learning experience – the most valuable data is performance data – having a huge amount of data helps build a model – data science is no help
o Aggregated and analyzed alternative data:
- 300 attributes on the application
- Separate borrowers into nodes
- Later 2,500 attributes from multiple sources
- Over 120 segments
- Data helped simplify the process & determine the score
- Merged alternate data with bureau data
- Fair equal Opportunity Act – the jury is out on what data you can use to deny credit, the actual underwriting decision
- Made more than 500,000 loans with single digit loses
- Partnered with Prosper, the first peer-to-peer lending marketplace, with more than 2 million members and over $1 billion in funded loans.
- Risk model design
- Loan valuation framework
- Valuation stress testing.
Today James’s focus is on Insikt Inc., a financial data analytics company that uses data for risk models to apply to consumer markets. He and his team are working on how to originate loans in the subprime market and how to create a more curated market for securitization – concerned with both bond performance & loan performance.
Panel Discussion, Moderated by Matt Turck, Organizer
- Banks are encumbered by regulation
- Alternative lending is only 5% of consumer finance
o In 5 years we’ll see a lot of new entrants
o More partnering with banks
- Transforming the bank system to be more transparent and customer friendly
- There are four different segments with lending opportunities
o Emerging markets
- Top ten global institutions will be big players in financial services
- Rates for consumers can be as low as 6.5%, average 12.5%; for business, 5.9%
- Rate risk is better through the use of data and marketplace dynamics drive interest rates down
- A virtuous circle continues to make credit more affordable and drives interest rates down
- Most credit cards are priced at prime plus
- Alternative lenders return a higher return to investor
- Rates rise in a better performing economy and defaults come down so we expect our proceeds to be stable.