Originally published on blog.andera.com by Melanie Friedrichs. Follow us on twitter @AnderaInc

A couple of weeks ago I was having dinner with a friend, and we got to talking about algorithmic trading, also known as high-frequency trading. Wikipedia defines algorithmic trading as the “use of electronic platforms for entering trading orders with an algorithm.” Algorithmic traders make money by identifying trades that will predictably yield an insignificant profit, and then using computers and leverage to execute those trades at a speed and volume high enough to make a significant profit.

Algorithmic trading is a relatively popular topic these days, primarily because it’s making a some people a lot of money, but also because algorithmic traders make money by leeching off the inefficiencies of the system, and are therefore everyone’s favorite scapegoat. Even Mr. Potter probably could have shook his finger at algorithmic traders without feeling like a hypocrite. However, defendants argue that by improving the price signal and reducing volatility, algorithmic traders do provide value, and I’m inclined to agree.

Despite its questionable morality, algorithmic trading has grown considerably over the last 20 years. From 2008 to 2011 about 2/3 of all US stock trades were executed by algorithmic trading firms, although this ratio has fallen over the last couple years as existing dealers, investment banks, and even individuals, have started to adopt the techniques of once-boutique algorithmic trading firms. And even though profits have also fallen as the industry gets more competitive, algorithmic trading is increasingly becoming a prerequisite to survive; when all your peers are trading with computers, you’re dead if you’re not trading with computers too.

At dinner my friend referenced a paper published in Nature in 2012 by a group of physics professors from the University of Miami called… (this title deserves a drumroll)…

Essentially, the abstract says that Wall Street is turning into one big robot park. Investment finance has fallen to the rise of the machines.

What this means is unclear, but I think it’s a good thing. Today many simple tasks, like placing calls, routing transfers, and applying for financial products (gotta put in an Andera plug somewhere), are done much more efficiently and reliably by machines. I’ve never been a big believer in “malicious intelligence,” my term for the idea that artificially intelligent machines will eventually turn on their creators, and of course the theme ofTerminator 3: Rise of Machines. For me the larger danger is that the algorithms will get out of whack, like during the “Flash Crash” of 2010, when an algorithm gone haywire created a huge swing in the stock market (but was corrected a few minutes later). But as long are there are appropriate safeguards in place, I don’t see a problem.

The flash crash on May 6th, 2010. Graph from CNN Money.

During this conversation I couldn’t help but think about what relevance the rise of the machines in investment finance has for personal finance. A lot of recent innovation in personal finance has been predicated on the idea that consumers want to be more actively involved in managing their money; that given they opportunity, they will obsessively check balances, track spending, and comparison shop. But more and more evidence shows that consumers really do not want to think about money at all:

According to Gallup, only 32% of Americans keep a budget (and I’m betting they don’t budget well).

This makes sense; no one except the miser gets utility from just having money, and they get even less from counting it. They get utility from the things they can buy with money. When it comes to managing money, all consumers really want to know, is, can I have it, or not? And possibly, if the answer is no, what do I need to be able to do to get it? Unfortunately for consumers, right now no one can really tell them that. They can check their balance and track transactions, but to figure out what they can spend they have to estimate a budget, predict incoming and outgoing cash flows, and make their own, unfortunately often incorrect, judgments.

A few companies have experimented with applying algorithms to the everyday decisions of the average consumer (good examples are Planwise, Pocketbook, and Simple’s Safe-to-Spend feature)  but they haven’t gotten very far. I’m thinking big here. Imagine going to dinner with a friend, pulling out your smartphone, selecting a category like “dinner with friends” and having your personal finance application spit out a recommendation like, “You should spend less than $30 to stay on budget” or “go ahead, splurge, you’re $100 over for the month.” With location tracking and pre-saved preferences, you might even be able to skip selecting the category or placing your order; you would just know you’re over budget when you get the spaghetti instead of the steak. Of course this would also be a cashless transaction.

Simple’s Safe-to-Spend feature.

Ok ok. That might be a little much. But do you get where I’m going? Just like algorithmic trading has taken the human effort and human error out of investment finance, algorithmic budgeting could take the human effort and human error out of personal finance. I think that the rise of the machines could be beautiful thing.