
Max Levchin’s Affirm Raises $275M to Make Loans - elvinyung
http://recode.net/2015/05/06/max-levchins-affirm-raises-275-million-to-make-loans/
======
7Figures2Commas
> Affirm makes an assessment of creditworthiness based on a person's name,
> email, mobile number, birthday and the last four digits of his or her social
> security number, as well as behavioral factors like how long he or she takes
> to remember all that information. If that combination doesn’t quite add up
> to a loan, Affirm may also ask borrowers to share information from other
> online sources, like a GitHub coding profile or a savings account that shows
> cash flow history.

It's going to be interesting to see how these lenders show compliance with
various aspects of the Fair Credit Reporting Act and Equal Credit Opportunity
Act.

The latter, for instance, prohibits lenders from denying credit on the basis
of race, color, religion, national origin, gender, marital status, etc. But
it's possible some of the data points used by these lenders will turn out to
be proxies for these attributes. There is already a lot of discussion around
how lenders are using "big data" and the associated potential pitfalls[1][2].

[1] [http://www.microfinancegateway.org/library/big-data-big-
disa...](http://www.microfinancegateway.org/library/big-data-big-
disappointment-scoring-consumer-credit-risk)

[2] [https://www.aclu.org/blog/ftc-needs-make-sure-companies-
aren...](https://www.aclu.org/blog/ftc-needs-make-sure-companies-arent-using-
big-data-discriminate)

~~~
grandalf
There are two scenarios: Someone whose FICO score indicates more risk than
Affirm's algorithm, and someone whose FICO score indicates less risk than
Affirm's algorithm.

Affirm chooses a rate based on its algorithm's prediction, and I believe worst
case just offers standard FICO-based rates... because it has a slightly
different business model, it has no incentive to ding customers with a $15 fee
if they pay the third payment three days late.

~~~
7Figures2Commas
You're oversimplifying the issue here. The point is that Affirm is apparently
using an amalgamation of potentially thousands of data points to make
decisions about creditworthiness. Some of these data points, including name,
email and mobile number, as well as behavioral factors, could very well prove
to be proxies for characteristics which the law prohibits lenders from using
to make credit decisions.

~~~
grandalf
Interesting. At some level things like income could be considered a proxy as
well... arguably the absence some of the more stringent consumer lenders, like
Chase, on one's credit report might predict membership in a disadvantaged
group.

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bbcbasic
Yes let's make it easier for youngsters to get themselves in the hock for that
hit of instant gratification. Get 'em on the never-never instead of teaching
them to save.

What I'd like to see is a disruption to banks to encourage people to save
properly, by integrating proper budgeting and accounting into the bank account
itself. I.e. walling money that is allocated for future expenses such as
insurance, utilities, presents etc. On the other hand with no extortionate
fees for going overdrawn. I'd love to see something like that.

~~~
hkmurakami
Serious question: Why would any financial institution implement something that
will reduce their revenues?

Followup question: How would a company be able to come in and offer what you
describe and offer users savings, and take a cut for themselves along the way,
so that the users and the new company wins, while the incumbent banks lose?

~~~
bbcbasic
Good question, one I was thinking myself!

Some banks offer something a little bit in this direction (at least in the UK
and Australia). I have heard of banks with 'savings pots' and online saving
planners.

For a bank it is probably good for them if you save. Eventually you will spend
the money and in the mean time the saved money makes them money because (due
to how the banking system works).

The way I can see this playing out is based on consumer demand. Banks have
little in the way of genuine USP. I mean they all pretty much offer the same
service and must compete on service, interest rates, fees, branding,
reputation etc. So offering a way to help people save could be a good USP for
the right kind of bank.

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sksk
>>..last four digits of his or her social security number, as well as
behavioral factors _like how long he or she takes to remember all that
information_. If that combination doesn’t quite add up to a loan,...

What I find really frustrating with most of these fintech companies is that
they claim they use all kinds of data. But they don't really disclose whether
this is used for 'underwriting' or 'fraud'. When it comes to underwriting
there are specific FCRA guidelines on reason codes. For example, if they
decline credit to a consumer because s/he did not type in their address fast
enough, they need to tell them that. That would make for a good sound bite for
the company.

Also, what if disabled people cannot type fast enough? That seriously touches
on discrimination. Too bad CFPB is not going to care about these companies
until they get to $10Bn in loan size.

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jliptzin
I have a hard time believing that Github commit history actually factors into
the loan process. Is the typical customer of a service like this likely to be
a programmer, let alone even know what Github is? Also, is there data that
shows that someone with a long commit streak is more creditworthy than someone
without a Github profile?

~~~
derefr
Presumably, Affirm is an application of the same "all-encompassing many-
sources-as-possible Bayesian-analysis engine" behind both Paypal's anti-fraud
and Palantir's anti-terrorism technology. As such:

> Also, is there data that shows that someone with a long commit streak is
> more creditworthy than someone without a Github profile?

That data doesn't exist; the precise job of this sort of correlation engine is
to create it. In credit analysis, you _start_ with a decent credit-worthiness
model of all your customers—derived not from predictions, but from how people
with a given feature-set did historically. Then you go looking for new feature
datasets to incorporate into your model which decrease its RMSE at predicting
split-subsets of your outcome data.

Github commit history could easily be one of these. I would guess it would be
a good prediction of both average employment tenure, and of the Big 5
Conscientiousness personality trait.

But I don't have to be right—I just throw the feature-set into the engine, and
it either finds signal in it and turns up its weighting in the model, or finds
that it's noise and turns it down to zero.

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sandGorgon
I'm kind of curious - is this new to the US market ? There was Paypal
BillMeLater, etc. In India - we have something called EMI (Equated Monthly
Installments) which a lot of credit card companies do for certain online
purchases (like mobile phones, etc.)

what exactly is new here ?

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walterbell
The article claims that credit assessment is partially based on _"..behavioral
factors like how long he or she takes to remember all that information.."_. Is
data entry latency being used as a proxy for human memory latency?

~~~
grandalf
It's just kitchen sink AI applied to as much data as possible, and two plays:
1) a trustworthy lender that doesn't short-term maximize, and 2) in some cases
a better rate due to the AI's data-driven prediction.

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NHQ
This but for small business loans instead of consumer product debt.

~~~
morgante
[https://www.fundera.com/](https://www.fundera.com/)

~~~
kevindavidcrowe
Basically, we are trying to do what Kayak, Priceline and sites alike did to
travel agents, but for loan brokers in the alternative lending space.

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dlp211
How is this different then PayPal Credit (formerly billmelater)?

~~~
JoshTriplett
[https://xkcd.com/918/](https://xkcd.com/918/) , but with PayPal in place of
Facebook?

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ajaimk
I wonder if the interest is charged from day 1.

~~~
grandalf
By law it is mapped to an APR which is disclosed to the customer, but there
are typically no late fees or other (typical) penalties.

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smil
>Because of incumbent banks’ endless surcharges, late fees and other abuses,
“there’s room for a product by young people for young people,” Levchin, 39,
said in a phone interview this week. “That’s what Silicon Valley’s good for,
and that’s what we’re doing.”

Levchin is not young, so I guess his startup is DOA.

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rfrey
Levchin _must_ have been having the reporter on. This is not serious.

 _based on a person’s name, email, mobile number, birthday and the last four
digits of his or her social security number_

Onomancy, hotmailancy, astrology, and numerology, respectively. Ok I made up
hotmailancy, but really. He just needed to add tiromancy and they could adopt
Wallace and Grommet as their spokespersons.

~~~
jordanthoms
Those form the primary keys to access your credit record, they aren't going to
be getting much data from them by themselves.

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mbesto
> _“There’s an unbelievable amount of information available on American
> consumers — so it’s not exactly Affirm that you should be freaked out about.
> Having said that, we’re probably some of the most ethical users of such
> information.”_

Rationally speaking, how is his "trust us" remark any different from the US
government NSA spying program's "trust us" remark?

~~~
JoshTriplett
You choose to do business with Affirm in order to get something from them that
you deem valuable enough to let them gather information about you, and they
tell you that up front.

You don't choose to do business with the NSA, you have no say in whether you
consider their "product" worth the cost, and they cover up their own
activities.

~~~
mbesto
> _You choose to do business with Affirm in order to get something from them
> that you deem valuable enough to let them gather information about you, and
> they tell you that up front._

True. But that makes the business model more compelling. It doesn't negate the
fact that it takes just one bad actor at Affirm to screw that trust up (just
as easily as it takes just one bad actor at the NSA to screw their trust up).

