Smart, non-techie people sought for new project

You read it right! I'm looking into a new and exciting project in financial services, and it requires 1-2 people that are not engineers but can handle technology, operationally minded but not necessarily with years of experience. Here's something I wrote a few years ago that captures the kind of people I enjoy working with (this is not for PayPal!, see after the quote):

What I’m looking for is results driven, quick thinking do-it-alls who want to be involved with new products, markets and risk challenges within Paypal. You should have the passion for consuming a lot of data and information, be able to learn quickly and identify and define trends in concise terms. You should be analytical and with a quantitative approach but not a data cruncher without any understanding of the big picture – we are playing at all fronts. Know or be able to learn how to drive processes through other people and organizations; working in ambiguous situations and coping with change is a must, as well as an ever changing operating rhythm. This is not your classic 9 to 5 and I’m not your classic 9 to 5 manager.

Experience is not a must (=graduates are also encouraged to apply), definitely not previous experience in risk management. However, please be an avid internet user, preferably a gamer in your past or present. Some security experience or tech savvy is a big plus – don’t get intimidated by developers, architects and tech talk. Impress me by having interesting hobbies out of work that you maintain although you are an aggressive achiever, and by having vast general knowledge (as in: you shout answers at “who wants to be a millionaire” while watching it on TV).

This is an excellent opportunity to be part of a founding team of a new startup that I think is very interesting, and to get a glimpse into the method and ideas that made FraudSciences, Analyzd, Signifyd (and hopefully this one as well) such a lucrative deal for investors, customers and acquiring corporations. This is also an opportunity for extremely smart people who aren't engineers and are looking for a way into startups and don't know how. Refer your best friends ;)

Please help me spread the word! Contact me directly for details.

NOTE: local SF Bay area folks highly preferred.

Even good things come to an end - Why I'm leaving Klarna

I wasn't sure I was going to write this post, but I've had several of these conversations in the past few weeks and putting my thoughts here is a good kind of closure.

Link to actual post: http://www.ohadsamet.com/2012/11/25/even-good-things-come-to-an-end-why-im-leaving-klarna/

The blog is moving - now for real

All,

Thanks for following the blog on blogger; I now moved to my own domain here: http://www.ohadsamet.com/blog/

Hope you'll continue following and sometimes even commenting :)

Ohad

It’s not about new connectivity technology; consumers don’t care

With the resurfacing of NFC based solutions driven by mobile providers and banks in various territories (and as if this weird late adoption isn’t a good enough indication to its irrelevance), I feel compelled to repeat my message about connectivity technology.

NFC was dead on arrival for payments, and the ongoing discussion about it is misleading and irrelevant. That’s for two reasons – first and foremost NFC doesn’t solve a problem, something I’ve been repeating for a while but has been discussed again lately. Handing your card over, waving your phone, who cares? There’s no consumer incentive to adopt this technology. On the other hand there’s (lack of) merchant adoption: NFC usually requires replacing or adapting your POS terminal, and the merchant has no value in doing so. A card network that really believes in NFC can push it by promising lower fees or other types of coverage to give merchants an incentive – that was tried and failed with 3D secure. Merchants hate conversion killing or just non-contributing features.

The same is relevant to other connectivity technology. I hear Jumio is growing, but most of its volume isn’t coming from its card.io based technology, that allows you to show your card to the camera to have it recognized. The reason? In the time it takes you to set up flash, take your card out, position it in front of the camera and add needed info you can type it in since you remember it by heart. Of course you do. So as I said here, there is not incremental benefit to anyone other than some marginal fraud prevention for merchants (which is not to be discounted, but is marginal, since it’s a conversion killer).

Disagree? Consider other solutions that area vying for market share: Google Wallet, Serve, and other sign-up-and-add-your-credit-card solutions that popped up lately. Consumers don’t have a problem with directly using card to pay for purchases – not offline, and increasingly not online (at least in the US). They don’t care how secure your solution is, they have plastic. They don’t care about coupons; they have rewards on their cards. They are not interested in a new financial relationship that’s based on their credit card. If you’re not solving a problem, you’re going to have to buy yourself some market share, which is exactly what’s happening, even with the larger providers.

Klarna talk at Finovate is live

The 7-minute demo I gave at Finovate together with Jakob Soderbaum is now on their website. Catch it here:

 

We pride ourselves in the success of the company and the numbers we are able to share. Indeed great job by the founders and the team!

Signifyd launches its Risk Management and Fraud Prevention training program

One of the questions I get asked regularly is "where do I learn more about fraud and risk management for payments?".

I usually recommend the MRC and an eBook called "Detecting Malice" for first steps but honestly, there's not a lot of material out there. That's why I started this blog in the first place.

Today, Signifyd is launching a new training program for risk personnel. I'm excited about it since it's the first program I saw that I can really relate to (I advise to Signifyd although I was not a part of creating the program). I hope it will draw a lot of attention to what this up and coming team, led by Rajesh Ramanand and Michael Liberty, is up to.

Good luck to the team, and please take a look at the program!

What's Missing in Data Science Talks

On January 28th, 2008, the $169M sale of Israeli FraudSciences to eBay's payments division PayPal was publicly announced. I was part of the 65 person crew and head of the analytics group at the time. FraudSciences became PayPal's Israeli R&D center and is still a thriving team spanning more than 100 people and providing great value to the company. Our story has even been mentioned on StartUp Nation, in an inspired-by-a-true-story style dramatization of events.

The sale and its ramifications is not what I want to talk about, though; what I do want to talk about is the events that led to that sale, and more specifically the test that PayPal ran us through. You see, PayPal had to see whether our preposterous claims about how good our algorithms were held true, so they threw a good chunk of transactions at us to analyze and send back to them with our suggested decisions. Long story short, our results had an upside of up to 17% over PayPal's own algorithms at the time, and the rest is history.

How did we do that, then? We must have had a ton of data. We must have used algorithm X or technique Y. We must have been masters of Hadoop. Wait - no. 2007. Nothing of the sort. Everything takes forever. To get to these results we didn't even use the two famous patents FraudSciences viewed as huge assets since they required some sort of real time interaction with the buyer. What we did have were roughly 40,000 (indeed) well-tagged purchases, good segmentation, and great engineered features all geared at very well defined user behaviors. What we had, plain and simple, was strong domain expertise.

Domain expertise, or lack thereof, is exactly my issue with the talk about Data Science today. Here's an example: I recently had a friend, a strong domain expert, rejected from a pretty nascent startup filled with very smart engineers since they didn't really know where to place his non-developer profile in their team. Were they wrong to not hire him? Maybe, maybe not. I can't judge. Were they wrong to make the decision based on coding skills? Most definitely. It's a very common passion for data and ML geeks such as ourselves to embark on the (in my opinion) hubris-driven task of building an artificial intelligence that will solve all problems, the Generic SkyNet. We neglect to admit the need for specific knowledge. It is then when discussions of volume and structure of data sets replace keen understanding of what people are trying to achieve - when complex tools replace user research. Unsurprisingly, these attempts either fail or scale down to take domain by domain. They can still take over the world - just with a different strategy.

When I read people on Kaggle, in itself an amazing website and community, list the tools they threw at a dataset instead of how they led with a pure analysis of pattern and indicators, I cringe a little. This is a craft fueled by excess - in space, in memory, in computing power, even in data. While often times highly useful, almost as often does it  make us miss the heuristic just in front of our eyes. I think that analysis and Data Science need to incorporate this realization as well, to become a real expertise.

Fraud detection and prevention and Credit issuance, the stuff we deal with on a daily basis at Klarna, are areas where this is an obvious issue. High fragmentation in geographies, payment instruments and products creates smaller training and validation sets than you'd ideally want. The need to wait for default or a chargeback limits the time between iterations. The presence of bad signals is scarce compared to other types of classification. Operational issues and fraudsters' strong incentives to hide (as well as abuse or "friendly" fraud) cause "dirty" performance flags. And still we have a shop that uses a number of instances per segment that Data Science teams would frown upon to make some accurate decisions. How is that? The same way FraudSciences gave PayPal's algorithms a run for their money - we use domain expertise to distill features that capture interaction in a way that automated feature engineering methods will find hard to imitate. We use bottom up analysis of behavioral patterns. We add a sprinkle of behavioral economics (but building a purchase flow is a completely different story).

This aspect of what we do is available to any Data Scientist out there - I've written extensively about finding domain experts. They're around you. Use them - and don't get hooked on the big guns just because they're there*.

*Well, only if you want to get better results quicker and are acting under market and product constraints. If you're a contributor to an open source project - carry on with your great work!