Machine Learning and AI: Taking Over the World…or Just Opening New Doors for Financial Services?

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Greg InksBy Greg Inks, Cloud Services Practice Lead

Everyone knows who Sarah Connor is…at least those of us who remember “The Terminator.” This iconic movie of the late 20th century told the story of a super computer (Skynet) that became self-aware and took over the world.

Advanced computing power and sophisticated programming combined to teach a computer to program itself. This resulted in what we would today call the ultimate cloud system. The movie depicted an elastic computing platform that had the ability to control everything from IoT (internet of things) devices like drones to advanced computational platforms for use in academia; from healthcare systems that could prescribe treatments or cures to everyday factories building everyday tools.  Does this sound familiar?

Yes, this was a sci-fi movie, but as we know, life imitates fiction. And with the leaps and bounds the Cloud is taking, we are witnessing these solutions coming to life. It’s called Machine Learning…and it is the hot topic of discussion these days.

But is it real? Is AI actually viable, or is it just a marketing tactic being employed by Cloud providers? And getting back to poor Sarah Connor…are computers poised to take over the planet?

To answer these questions, we need to start with a definition of machine learning. Then we will discuss where it actually is today and where it’s taking Financial Services.

A Definition of Machine Learning

In actuality, machine learning is an “older” term often used to refer to nearly anything that spikes an individual’s emotional response to a computer thinking like a human. Modern-day computer science might define machine learning as a computer with the ability to learn or operate without being programmed explicitly. There is a very subtle yet important difference there.

For a computer to think like a human thinks, it would need to have curiosity—a problem that it wants to solve—and the cognition to ask a question, analyze to determine an answer, make a judgement about the validity of the answer, take action, and repeat the process. This is often associated with self-awareness – something that living organisms have (through either voluntary cognition or involuntary genetic response to stimuli). Computers, at least as far as I know right now, lack this characteristic.

But…computers are rapidly becoming powerful enough to drive the cycle of discovery much faster than humans (doesn’t that sound scary?). That is what we refer to as machine learning. In short, if we can give a computer a model by which it can analyze data and make a deterministic evaluation over possible answers, it can take action on those answers and even learn from the results as to the fit of the answer against the data. If you can do this over a sufficiently large data set, so that the statistics (yes, statistics, along with linear algebra and differential equations) supply an answer that becomes relevant against a data model, you have actually trained a model against your data without writing any code. Computers are fantastic at applying patterns in this manner and now have the power to drive these calculations a break-neck speed over internet-scale amounts of data. Humans, however, cannot!

Machine Learning: A Financial Services Example in Real Life

Ok…this sounds complicated, so let’s work through a financial services example:

You buy your favorite stock, and your advisor or trading partner now has two distinct data sets to use—one set is all about you (name, age, income, portfolio, purchased amount, etc.) and the other is all about the stock (performance, cost, trading characteristics, demographics that bought it, etc.). Most financial advisors and trading partners will then try to match you (based on your personal attributes) up with other stocks with similar attributers to that of which you just purchased. Sometimes this results in good decisions (gains), sometimes in bad ones (losses); either way, a human is reading reports and interpreting the data so he or she can plan to recommend a course of action. If this person is good at what they do, they are also learning from their bad actions and adjusting their decision model. Still with me?

It’s not much different for a computer. Some human loads an evaluation model into a system like Azure’s Machine Learning in the Cortana Data Platform. The model is then trained against thousands or millions of people that purchased stocks as well as all the stocks that have been traded for days/weeks/months/years.

I refer to this as “trained” because Cortana uses the model to calculate deterministic answers to the various sets of properties it has access to, and determines which ones really made a difference in selection. In short: Cortana has now learned what people like you and I purchased that made the types of gains we wanted, and can suggest additional stocks that are tailored to us based on our own individuality. All the while, it is continuing to train the model against new data that is coming in, and discarding old answers that are no longer statistically relevant. It feels like artificial intelligence, but it is really math – done on such a scale that no human could possibly do those calculations real time – almost like magic.

Are Computers Going to Take Over?

No. What makes the computer so great at machine learning is obviously Cloud-scale computing power. But people have something computers don’t—emotions. Computers can’t take into consideration all those little things we just “know.” And they lack the ability (at least today) to see the trends outside of the explicit model that was setup or ask the follow up questions about “why.”

Until we can build systems that can apply a machine learning algorithm on top of the results of another machine learning algorithm, and build a neural network of computation, the system will not be able to evaluate and predict all behaviors – for now. Given a sufficient amount of time, enough computational power, and the right type of recursive evaluation models, computers will likely be able to develop curiosity and start to ask its own questions.

But Financial Services Can Benefit NOW

For now, however, we can take advantage of machine learning to make ourselves better at our existing jobs. We are sitting on tops of petabytes of data; the history of everything that we have done over the decades. Any government or agency has burning questions that can be answered with the right machine-learning models.

The ability to accelerate our decision cycles to address our own curiosity is available to us right now—through the Cloud.

Do you want your firm to go beyond traditional business intelligence and analytics? To make decisions based on what’s coming rather than what has already happened? We can help, offering expert Cloud Services that will help you develop and execute your Cloud strategy, including native cloud development that can bring Machine Learning into that strategy.

Ready to get started? Talk to the Cloud experts at AKA about how you can start capitalizing now on the power of Azure and predictive insights.

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