5 ms·

"Likewise, your credit card company and the phone company have records of what you did, as opposed to what you plan to do next." Sorry to bust your bubble, but

by icefox 16y ago

"Likewise, your credit card company and the phone company have records of what you did, as opposed to what you plan to do next." Sorry to bust your bubble, but your CC company has already given talks about how it knows when you are about to move, when you plan on getting a divorce, when you are trying to have a baby, when you got actually going to have a baby and plenty of other personal thing... I can only imagine how fun it would be to sift CC data for patterns and cash in on what I find. A calendar of things I WANT to do isn't useful, what is valuable is what I WILL be doing. I really want to build a factory five GTM super car and will happily put it on my want to do next year, but in reality unless someone pays me to built one for them I can't do it for a while. Once my CC starts seeing car parts they will know that it is actually something I am doing and not something I would like to do. For now my CC company knows last weekend I rented a truck, bought some house supplies and changed my address from Chelmsford to Waltham. Now would be a perfect time to sell that information to local resistants and shops that are around my new house. Or seeing as the new address is a recently purchased house now would be the time to sell that info to the home repair stores which I have not already started going to. Edit: Anyone in the CC industry allowed to comment further on this topic? No doubt it would be a neat place to work.

gacba 16y ago
I don't work for a CC company, but I did work with predictive analytics regarding churn in mobile phone and cable companies a decade ago. It's not hard to back-mine the data to establish certain patterns that happen before major events. That's how we predicted churn at a major provider (rhymes with "Blecks-dell") with 90% accuracy. There's no reason that same logic could apply to a rich data set like CC transactions. The tricky part is defining what "events" you want to detect, then getting a set of data with those events for training on the network.
- patrickas 16y ago
  That sounds interesting! I can't think of any types of things to check for to predict a user is about to churn! Care to give more examples? The closest I could think of that might work is a neural network that would be fed data for people who churned and people who didn't but even then I cannot think of useful types of data to be fed!
  - techbio 16y ago
    What data do you have? If a predictive system performs with that data, QED.
  - matwood 16y ago
    One of the easiest today is using universal default. If you happen to pay your water bill late then that tells me that you're having money problems so I can expect other bills to start to falter. Some CC companies will actually raise their rates on you proactively assuming you're about to default. I think that's crap, but it's part of having everything so connected. Other big ones that we found are simply changes in behavior. The person who pays their bill late every month is fine, and if they pay late enough to get the late charge even beneficial. The person who pays on time every month, but is suddenly late, big red flags. A person who suddenly starts using their CC card for groceries, but never did before is another example. People also tend to live near similar people. If you're a company providing a service to many people you can start to build models of neighborhoods. If both my neighbors foreclose on their house there is a higher likelihood that I'm also going to be running into money problems.
techbio 16y ago
As appealing as the opportunities appear, my naive guess is that "neat" is superseded by "regulated" but this makes me wonder... while finances are about as graphically personal as information can be, the optimization potential for billions of people, each with hundreds of choices, is astounding. How could this computation be performed locally, in a directly parallel device, and in a way that protects the consumer? How about smart (real: assistive AI) cards as a disruptive technology?

by icefox 16y ago

gacba 16y ago

I don't work for a CC company, but I did work with predictive analytics regarding churn in mobile phone and cable companies a decade ago. It's not hard to back-mine the data to establish certain patterns that happen before major events. That's how we predicted churn at a major provider (rhymes with "Blecks-dell") with 90% accuracy. There's no reason that same logic could apply to a rich data set like CC transactions. The tricky part is defining what "events" you want to detect, then getting a set of data with those events for training on the network.

patrickas 16y ago
That sounds interesting! I can't think of any types of things to check for to predict a user is about to churn! Care to give more examples? The closest I could think of that might work is a neural network that would be fed data for people who churned and people who didn't but even then I cannot think of useful types of data to be fed!
- techbio 16y ago
  What data do you have? If a predictive system performs with that data, QED.
- matwood 16y ago
  One of the easiest today is using universal default. If you happen to pay your water bill late then that tells me that you're having money problems so I can expect other bills to start to falter. Some CC companies will actually raise their rates on you proactively assuming you're about to default. I think that's crap, but it's part of having everything so connected. Other big ones that we found are simply changes in behavior. The person who pays their bill late every month is fine, and if they pay late enough to get the late charge even beneficial. The person who pays on time every month, but is suddenly late, big red flags. A person who suddenly starts using their CC card for groceries, but never did before is another example. People also tend to live near similar people. If you're a company providing a service to many people you can start to build models of neighborhoods. If both my neighbors foreclose on their house there is a higher likelihood that I'm also going to be running into money problems.

techbio 16y ago

As appealing as the opportunities appear, my naive guess is that "neat" is superseded by "regulated" but this makes me wonder... while finances are about as graphically personal as information can be, the optimization potential for billions of people, each with hundreds of choices, is astounding. How could this computation be performed locally, in a directly parallel device, and in a way that protects the consumer? How about smart (real: assistive AI) cards as a disruptive technology?