Sift Science (YC S11) raise $18M to stop credit card fraud with machine learning

Calling it &#x27;lose-lose-lose&#x27; doesn&#x27;t actually make sense. 1) They lose the TV; 2) they lose the $1000 dollars they obtained in the transaction; 3) the chargeback negatively impacts relationship with the card issuer.<p>By that accounting, a legitimate sale would count as a &#x27;lose&#x27;, since they lose the TV.<p>&#x27;Lose-lose&#x27; would be fine, but even by the standards of hastily written press releases, this is kind of silly.

I run a product in this space (<a href="https://www.merchantprotector.net" rel="nofollow">https:&#x2F;&#x2F;www.merchantprotector.net</a>) and I&#x27;m quite impressed with Sift Science&#x27;s pricing.<p>No charge for the first 10k transactions&#x2F;month is impressive.

I can think of some simple things FIs can do to prevent fraud. For example they could leverage your cell phone&#x27;s GPS at the time you make a purchase to make sure you&#x27;re actually in the store where the purchase is coming from. For online purchases, they could text you a confirmation that you were the one who made the purchase. Just some simple things that seem a lot less complicated than machine learning that we haven&#x27;t tried yet.

Just off the top of this article - BestBuy wouldn&#x27;t be liable for that $1000, it is the FIs liability, if we&#x27;re talking about a transaction at a physical terminal, not CNP.<p>Edit: FIs also have to follow rules and regulations to monitor and predict fraud activity.

The biggest problem with ML is that it takes time for it to react to the new fraud patterns&#x2F;schemes. While rules engines have their limitations, they also have one big benefit: they allow to block recognized fraud really fast.<p>So the actual question here is whether the ML for detecting fraud is better than a flexible rules engine and goods analysts&#x2F;statisticians. In my personal experience, statistical analysis and anomalies detection effectively handles majority of the fraud. I would be interested to see a more detailed analysis of Sift Science performance with some numbers for false positives&#x2F;false negatives for example, though (of course) it is probably proprietary information.

Awesome stuff. I use sift science, in addition to some basic fraud prevention, and what they are doing has allowed me to sleep easier at night.<p>Kudos to the Sift Science team!

Is the fact that this is based on &quot;no rules just data&quot; even sensible? Or is it an artifact of bad reporting?<p>Many Credit card fraud prevention systems such as the Falcon Fraud Manager use both Rules and ML (Neural Network modelling) to tackle this issue. I am not sure that a purely data centric approach with no rules even makes sense.

Anyone care to share their experience with Sift Science? How well does it actually work for you?<p>I have been integrating it for a day or so. The documentation is slightly confusing and they&#x27;ve had a few minor bugs in their UI, but support has been really good thus far.

When do you think credit cards &#x2F; bank accounts will become push transactions vs. the pull transactions by a few trusted banking agents as they are now. How much will that reduce fraud?

why cant credit card transactions be mandated to go through a second layer of auth that is <i>not present</i> on the card? in india, the banking regulator RBI, forced this a few years ago and the CNP fraud rates tanked to negligible levels. All domestic transactions now go through a 3d-secure or OTP process.