Episode 15 – Transparency and Reputational Risk

Episode 15 – Transparency and Reputational Risk


Hello everybody and welcome to
the Data Science Ethics Podcast. I am Marie Weber and today
I’m here with Alexis Kassan. We are talking about a case where an
algorithm was used by Chase Bank to deactivate some credit cards and the
implications around that algorithm and those decisions. The reason why we’re talking about this
is because it effects how people can access credit and the idea that somebody
knows what parameters are allowed in terms of them being able to
keep access to their credit. So a lot of times credit card companies
will be looking at their list of clients and looking at the different attributes
that make them a good cardholder. So Lexy, can you talk through a little bit about
what some of those different parameters are that credit card companies might use
to look at their clients and identify who is a good credit card holder and who
potentially might be a risk that they’d want to review? Sure. Before I do that, let’s back up
and actually talk about what the case. So in this instance, Chase Bank
has a premier tier credit card, called Sapphire Reserve, in
which there are better benefits. It’s an expensive card. It’s
got a pretty hefty annual fee. It’s for very high credit
score individuals who are
issued fairly substantial lines of credit on this card. It has accelerated earnings in particular
categories and things like that. One of the areas where a number of
card holders had voiced concerns was on Facebook and other online forums stating
that they had received a letter from Chase saying that their
card was being deactivated. Through sort of this group discussion, they determined that, in most cases, it was because they were shuffling their
credit balance from card to card or they had made some large purchases and
tried to game the point system or they had tried to use a very large
number of the benefits of the card, which while they are there, there’s a certain amount of breakage or
a certain amount of understanding from the bank that not everyone’s going
to use every one of these benefits. So when somebody starts using a
very large number of the benefits, they get a little skeptical. I’ve also heard that from, for example,
in this case, Chase’s perspective, they view those people as basically
being free loaders and they’re not paying interest. They’re not
bringing revenue into Chase. So then they look at it and be like, well they’re getting the benefits
and that is how we set up the card. But they’re definitely utilizing it more
than we expect an average cardholder to use it. Right. So for example, if they are constantly paying off
that balance or especially if they’re balanced, transferring the balance away, that might be an indicator that
they’re kind of gaming the points. There was one person though on these
forums that didn’t seem to fit the same pattern, who had also received a letter
stating that their card was going to be deactivated and they
couldn’t figure out why. They were not shuffling
their card balance. They weren’t doing a lot of points gaming. They weren’t really using a lot of the
other benefits of the card. They were, in all respects, seemingly
a great cardholder. They paid less than their balance, but
more than their minimum every month. They were employed. Good
track record of payment. All the things that typically you
would think a card company would want. And yet they got this letter that they
tried to call to figure out what was going on and they were given
no information whatsoever. So Business Insider got
involved to help investigate. What they found was that, completely outside of this
person’s credit history, completely outside of their
payment history, all of it, they had been involved in a lawsuit that
was for the company they worked for. The company settled a claim with
the federal government regarding misrepresentation of its software.
Even though they’ve denied wrongdoing, they settled to avoid this
big, expensive trial case, and some of the employees, including
this person, had to personally pay a fee. The fact that this person’s name was
associated with this firm was used as grounds to cancel their credit
card because of reputational damage So, when you say reputational
damage, how is that defined? A number of very large fraud cases and
very high profile class action lawsuits have made it more likely that banks invoke
this reputational concerns reason for cancelling cards that they feel
would reflect poorly on the bank. This was used to close accounts
related to certain industries. For example, it might be related to it’s
all entertainment or gun
shops and things like that. It could be for other things
where the bank feels like, if they’re seen as supporting this
industry or supporting this business, it could cause reputational
damage to the bank. So from a data science ethics perspective, do you have a sense of when
this reputational management
may have started to be incorporated into some
of these algorithms? It’s been around for awhile. There’s been an increased concern probably
since at least 9/11 with regard to anti money laundering. The other big one from a fraud perspective
was the Madoff scandal that happened several years ago. Any of these where there’s an opportunity
that a bank would be involved in something that, even if it isn’t associated with terrorism
or associated with some other bad cause, if it’s fraud and there is
the possibility for a large impact, the bank will try to cut ties. In
this case, it may not have been fraud. It may have just been kind of
an overabundance of concern, but these types of algorithms now
have more data available to them. More often than not, when you’re
first applying for a credit card, a company will look at your credit
history. Look at your payment history. Look at your current debt
outstanding. Look at your income. Try to calculate your
debt to income ratio. If you’ve been sent to collections before, if you’ve had a bankruptcy in the last
several years, so on and so forth, How many accounts you have. How
many inquiries there have been. Exactly all the things you
can see on your credit score. That’s traditionally what they use to
make a decision when they’re first giving you an account. However, they’re still
keeping tabs on what you’re doing, so they’re still looking
at your credit report. They are trying to determine if
you’re still a good credit holder. They’re also looking for any signs that
you might be involved money laundering. There are a lot of things that go
into an anti money laundering program, but in addition to your
transactional history, what this really brought to light is
that they’re looking outside of their own collected data to what else is going on
more broadly with their customer base to determine if that person is still
a good customer. For example, scraping Lexis Nexus and identifying legal
cases in which someone was named as a defendant in a lawsuit. They were able
to see that that person was involved. They were named. They settled. They
had to pay a penalty to the government. And they said, nope, we’re not going
to continue to give you credit. As a consumer, we rely on what we think is going to
be the information that they have, which is our credit report. We’re always kind of told the credit
report is the be all end all of whether or not you get credit and everyone focuses
on their credit score and so forth. And like we were just mentioning earlier, there are certain things that you’re
typically told our factor that they are looking at for your credit score and
there are various different parameters. So trying to follow all
of the best practices. Those are all different factors that
people do have some control over and are trying to use to improve
their credit score. And so it’s very interesting to find out
that there are these other parameters even outside of that that can effect
your ability to have or maintain a credit account. The reason that I think this ties into
data science ethics is really one of data access. It’s the more
general idea of privacy, but also the availability of information. That we as consumers of credit, we as customers of banks, presume that banks are going to use
financial information and we give them access to that. Right. But you don’t always think about what
all the information is that they could potentially use about you from the
broader internet and available data. This person was penalized
by having their card, deactivated for a case. They thought that they were doing the
right thing and they were told by their employer, we need to settle this.
We need to just do it out of court. It’s going to be easier in the long run. We’re not saying that
we did anything wrong, but we need to do this and they are
still being penalized because that information is public domain.
It’s publicly available. There’s a misconception that only certain
information can be used against you by a corporation and that’s not the case. Anything that’s out there could
potentially be brought in. While that might sound far-fetched, one of our future episodes is actually
going to go into how this is playing out in China where this type of thing, people’s activities both online and
offline are being taken into account in terms of how people have access
to credit and other systems. So the idea here is that one is consumers. It’s something to be aware of and then
to as people that are involved in the data science community, thinking about the ethical implications
of this and it goes back into the types of data that you include, the types of business decisions that
you’re making and ultimately how you’re constructing the data and
pulling everything together. Absolutely. I’m making sure that people are aware
that you’re gathering this information. There’s a certain amount of informed
consent that we give when we first sign up with a credit card that says, yes, I
agree that you can pull my credit report. I don’t agree that you can go do a
document search for everything I’ve ever, ever done. This is an area where the
boundaries haven’t been defined. There’s going to be, on
the side of the consumer, an awareness that this is happening.
And then, on the side of the companies, explaining how it’s happening and why
it’s happening and being a little bit more transparent about. The idea that somebody can just lose a
credit card and not have any explanation can be very jarring for people. So even when the financial crash happened, there were a lot of people that all of
a sudden started having credit cards, the activated and they
weren’t necessarily told why, which is part of the reason why now
credit card companies have to say why a credit card is being cancelled. This is a type of situation where
communicating that to the customers and customers also understanding how those
decisions are made is really important for everybody to be able
to have confidence in the
system and understand what’s going on. It also makes me think about some of the
GDPR regulations that were put in place in Europe. In GDPR, one of the core tenets is that you
have to have fully informed consent, meaning that you have to be told
exactly what’s being collected, exactly how it’s being used.
It has to not be in legalese. You have to agree. Now we can get into a lot of as
to whether or not people read it, even though it’s not legalese, even though it’s not pages upon pages
of end user license agreement and so forth. But the idea behind it is that you have
more information about what the company is going to gather in terms of data
and how they’re going to use it. And even here in the US with the consumer
protection laws that have been put into place, credit card agreements and things like
that have been updated so they’re easier to understand. They’re clearer.
Less legalese, that type of thing. Sure. However, I don’t think any of them specify we have
the right to cancel your card based on a search for lawsuits pending
with your name on them. Oh absolutely agree. Absolutely agree. It probably says something
along the lines of, we have the right to cancel your card
at anytime for any reason, which, while general, means that these
kinds of questions can come up. But when this information comes to light
about how someone has used data they found about a consumer in this manner, it really does shine a light as to
how much these companies are gathering information. How much
they’re gathering data. And how they’re using it for
and against their customers. Yeah, I think that’s probably a good
thing to also touch on is that, while it might sound like we’re being
very critical of Chase, in this case, for using this type of publicly available
information to close a credit card account, I think it is also important to understand
the business case that they’re trying to solve in terms of trying to not let
these reputation risks bring down the bank. With how much banks have had to deal
with recently in terms of reputation on multiple different levels, there’s obviously sensitivity that the
banks are trying to be proactive in terms of building their reputation and
building trust back with the public. Yeah, and JP Morgan Chase has been called out
numerous times and I’ve put in place some of the more stringent policies
for reputational risk mitigation. They’ve had a number of times where they
had made a decision to cancel accounts for a given business, for example, and then reverse course because
of public outcry and so forth. When you’re one person, there’s only so much public
outcry for you for a credit card. JP Morgan Chase, which is a
bigger company, have chase bank, has a lot of reason to put in place
these practices and to gather this information. There is
sufficient reason though, also to be more
transparent, to your point, about what they’re gathering, how they’re
gathering, what they’re using it for, and so on. Makes sense. Well, this has been our quick
take on the evolution of the
algorithms that Chase uses to evaluate the credit card holders and
if they should be able to keep their accounts if they find something that
would potentially have a damaging reputation associated with it. Thanks everybody for joining us for
this episode of the Data Science Ethics Podcast and exploring
this news article with us. Thanks so much. We hope you’ve enjoyed listening to
this episode of the Data Science Ethics podcast. If you have, please like and
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When we discussed model behavior, this podcast is copyright Alexis
Kassan. All rights reserved. Music for this podcast
is by Dj Shaw. Money. Find him on soundcloud or
Youtube as Dj Shaw Money Beats.

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