Your kid sister and younger cousin are unbanked and using alternative financial services June 6, 2012Posted by jeremyliew in financial services, unbanked.
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If you think it is the fringes of society that are underbanked, you’re wrong. As USA Today notes:
Nearly half of young adult households, those ages 15 to 34, are considered under-banked, according to a survey by the Federal Deposit Insurance Corporation in 2009, the latest year for which there is data.
Why is that? It’s the great recession of course, plus the rising cost of college. As the NY Times notes:
With more than $1 trillion in student loans outstanding in this country, crippling debt is no longer confined to dropouts from for-profit colleges or graduate students who owe on many years of education, some of the overextended debtors in years past. As prices soar, a college degree statistically remains a good lifetime investment, but it often comes with an unprecedented financial burden.
About two-thirds of bachelor’s degree recipients borrow money to attend college, either from the government or private lenders, according to a Department of Education survey of 2007-8 graduates; the total number of borrowers is most likely higher since the survey does not track borrowing from family members.
The problem is even more pronounced for teens. As Christian Science Monitor notes:
The Great Recession can explain some of the decline in job opportunities for young people. During the mid-2000s, the overall teenage unemployment rate ranged between 14 and 18 percent. Then, the downturn hit and teen unemployment began to rise, peaking at 27 percent in October 2009 (overall unemployment peaked three months later at 10.6 percent). Since then, the recovery has created more than 1 million new jobs for adults and brought the unemployment rate down to 8.2 percent. But there’s been no recovery for teens. For the past 41 months, the national average unemployment rate for teens has remained above 20 percent – a postwar record.
Think Finance recently did a survey of just these Millenials (18-34 year olds) to find out how they are dealing with their financial situation. The answer is that they are turning to alternative financial products, ranging from prepaid debit cards to check cashing to emergency loans, and this behavior is typically uncorrelated, or positively correlated, with income. For example, people making $50-75k were 50% MORE likely to borrow from a payday lender or installment lender than those making less than $25K. From their press release:
The survey found that several alternative financial products were used at similar rates regardless of income level, debunking the myth that only the poorest rely on alternative financial services. Millennial respondents with annual incomes significantly higher than the national median of $39,945 (U.S. Dept. of Commerce, Bureau of Economic Analysis) reported using alternative financial services at rates similar to peers who earn well below the national median. Products and services used at similar rates by Millennials at both the higher and lower ends of the income range include:
- Prepaid debit cards – 51 percent of those making less than $25,000 in annual income reported using prepaid debit cards within the last year. The percentage was the same for those who earned $50,000-$74,999.
- Check cashing services – 34 percent of respondents who earn less than $25,000 reported using check cashing services, while almost as many in the $50,000 – $74,999 range (29 percent) turned to check cashers.
- Rent-to-own stores – 15 percent of respondents making less than $25,000 and 17 percent of those who earn $50,000-$74,999 reported using rent-to-own stores.
- Pawn shops – 29 percent of respondents who earn less than $25,000 reported using pawn shops compared to 21 percent of respondents making $50,000 – $74,999.
Surprisingly, some of the alternative products that showed significant differences in usage across income level were more heavily used by mid – high income respondents than low income respondents. These products include:
- Emergency cash products – Usage of payday loans, cash advance and other emergency cash products was higher among people making $50,000-$74,999 (22 percent) than those who earn less than $25,000 (15 percent).
- Overdraft protection – 58 percent of respondents making $50,000-$74,999 reported using overdraft protection compared with 31 percent making less than $25,000.
- Bank direct deposit advance – 37 percent of respondents who earn $50,000-$74,999 reported using bank direct deposit advance compared with 22 percent of respondents who earn less than $25,000.
- Money transfer service – 39 percent of respondents who earn $50,000-$74,999 used money transfer services within the last year compared with 29 percent of those who earn less than $25,000.
“Stereotypes that paint users of alternative financial products as poor and uninformed are simply not accurate,” said Ken Rees, CEO of Think Finance. “This study confirms that young people across the spectrum have a need for the convenience, utility and flexibility that alternative financial services provide.”
USA Today provides some additional color on how the convenience and flexibility of some of these alternative financial products is driving their usage:
For a generation that grew up accustomed to instant gratification, it makes sense that young adults use alternative financial services, even if they come with a higher price tag but make cash available immediately, says Joe Wilson, a wealth management adviser at financial services firm TIAA-CREF and a Millennial himself. “Most things are to us readily accessible and convenient,” the 32-year-old says.
Ammy Orozco, 30, who works as an executive assistant at a Check Cashing USA branch in Miami, has a checking and savings account with Bank of America but often chooses to cash checks at work instead. She says she’d rather pay to cash a check immediately than pay for gas to drive to the bank. She has also taken out payday loans in emergencies. She’s tried to get a loan from the bank, but it was “stressful.”
“They wouldn’t confirm right away. … You’re there sitting and you need the money, and you’re like, … is this going to happen or not?”
It’s this big and growing market that gets me exciting about the innovations in financial services that are starting in the underbanked, and the disruption that big data and machine learning are driving that are creating better products for the underbanked. We are investors in Zestcash for this reason, and continue to be excited about more opportunities in this space in other products and geographies.
Big Data + Machine Learning in Insurance June 4, 2012Posted by jeremyliew in big data, financial services.
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I’ve posted in the past about how Big Data + Machine Learning is disrupting lending, and about how this disruption in financial services often comes from below, from startups targeting the unbanked. The Economist notes that big data + machine learning is changing underwriting at even big insurers:
At least two big American life insurers already waive medical exams for some prospective customers partly because marketing data suggest that they have healthy lifestyles, says Tim Hill of Milliman, a consultancy that advises insurers on data-mining software systems.
The software picks up clues that are unavailable in medical records. Recklessness in one part of someone’s life is a pretty good signal of risk appetite in others, for example. A prospective policyholder with numerous speeding tickets is more likely than a safer driver to end up with a sports injury. The software also detects obscure correlations. People who frequent ATMs so they can make cash payments tend to live longer than those who prefer writing cheques or paying with credit cards, it turns out. People with long commutes tend to die younger. Why this should be is not clear: some speculate that ATM users tend to be more spontaneous types, who like to have cash in their pocket and whose lifestyle may be more active; others hypothesise that sedentary commutes mean less time to do something healthy in the evening.
Interestingly, the advantage in using new sources of data to underwrite appears to lie more in cost reduction and speed to decision than accuracy:
But manual underwriting with medical tests can cost hundreds of dollars and, according to one estimate, drags on for an average of 42 days in America and Europe. That gives potential customers ample time to talk to a competitor or walk away. Automated underwriting can cost a tenth as much and be done once a human reviews the software’s recommendation.
Much of this is still in the anecdotal and experimental stage, but it is exciting to see that even big insurance companies can embrace new ideas.
More on data enabled underwriting April 30, 2012Posted by jeremyliew in big data, financial services.
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Check out my guest post on data enabled underwriting at American Banker.
How Big Data is changing the lending industry February 27, 2012Posted by jeremyliew in big data, financial services.
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I have a post on PandoDaily today about how big data +machine learning is reshaping lending and underwriting. I talk about some of the leading players in the space, including Wonga, Zestcash*, Klarna, and others, and why there is so much opportunity to create hugely disruptive companies in this space. Check it out!
* Zestcash is a Lightspeed portfolio company.
Data exhaust moves beyond targeted marketing and into financial services decision making November 27, 2010Posted by jeremyliew in data, financial services, marketing, targeting.
Fascinating article in the WSJ a couple of weeks ago about how the big insurance companies are testing using data profiles to identify risky clients. Using the data is potentially an alternative to the costly physical exams currently used to underwrite health insurance policies:
In one of the biggest tests, the U.S. arm of British insurer Aviva PLC looked at 60,000 recent insurance applicants. It found that a new, “predictive modeling” system, based partly on consumer-marketing data, was “persuasive” in its ability to mimic traditional techniques…
Making the approach feasible is a trove of new information being assembled by giant data-collection firms. These companies sort details of online and offline purchases to help categorize people as runners or hikers, dieters or couch potatoes.
They scoop up public records such as hunting permits, boat registrations and property transfers. They run surveys designed to coax people to describe their lifestyles and health conditions.
Increasingly, some gather online information, including from social-networking sites. AcxiomCorp., one of the biggest data firms, says it acquires a limited amount of “public” information from social-networking sites, helping “our clients to identify active social-media users, their favorite networks, how socially active they are versus the norm, and on what kind of fan pages they participate.”…
Acxiom says it buys data from online publishers about what kinds of articles a subscriber reads—financial or sports, for example—and can find out if somebody’s a gourmet-food lover from their online purchases. Online marketers often tap data sources like these to target ads at Web users.
Not everyone is comfortable with this approach. Some regulators have raised potential concerns:
“An insurer could contend that a subscription to ‘Hang Gliding Monthly’ is predictive of highly dangerous behavior, but I’m not buying that theory: The consumer may be getting the magazine for the pictures,” says Thomas Considine, New Jersey’s commissioner of banking and insurance.
I think I’d bet against Mr. Considine on this one.
I’m fascinated by the idea of using publicly available data to make better underwriting decisions, whether for insurance or for lending. This isn’t a new idea. Student loans first became a growth industry when someone decided that using a students major (pre-med vs liberal arts) or GPA could help them decide who to lend to and how much. But as the amount of data available has exploded, whether directly reported (e.g. on social networks), inferred from behavior (e.g. web surfing and ecommerce habits) or volunteered as part of an application (e.g. bank account log in info, as supplied to Mint, that can show regularity of income and cash payments), a financial instituions ability to underwrite more individually goes well beyond FICA scores.
I’m interested in any companies looking at doing something like this. Email me.