What Businesses Can Learn About Financial Industry Algorithms

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Today, there is a lot of debate about the use of algorithms in the context of companies. Some argue that their use makes it easier for companies to systematically fire employees; others feel that companies place great trust in what they see as a black box, unknowable and irresponsible.

Yet all of this discussion seems to ignore the fact that one of the first industries to start using algorithms strategically, the financial industry, is also notoriously risk-averse when it comes to adopting new technologies. In fact, many financial institutions use algorithms to make high-risk decisions involving large sums of money – perhaps the highest vote of confidence a company can give.

As the CEO of a company that uses deep learning to create custom marketing algorithms for players in every industry, including finance, I’ve seen firsthand the impact an algorithm can have when helping companies achieve their goals and accelerate their growth. It’s no exaggeration to say that algorithms have been a game-changer for the financial industry. They not only allowed big institutions to make money, but they also allowed individual players to make a name for themselves. Their successes are a testament to the power of machine learning, and they are an example for other industries looking to reinvigorate themselves.

How Algorithms Transformed the Financial Industry

The first quantitative hedge funds appeared in the 1980s, and their influence has only grown since then. One of the most common ways banks use algorithms is to set the parameters for a transaction to occur. Traders can create an algorithm that instructs the system to buy a stock when it reaches a certain price or sell it if it drops by a certain percentage. Although these algorithms are not always powered by machine learning, they are relatively common in the trading world and can even be used by those looking to invest in stocks on their own.

More sophisticated versions of these algorithms can incorporate machine learning to take into account all the factors that could affect a stock’s price, from world events to changing trends. For example, last year JP Morgan launched what it calls its “deep neural network for running algorithms.” It is a neural network that combines its existing forex algorithms into a single, highly optimized package.

While financial institutions use machine learning for its data analysis capabilities to identify potential opportunities in the market, they still often leave it up to humans to choose which opportunities to ultimately pursue. But as the Economist points out, there is also another way to use machine learning: to design new investment strategies from scratch, without having to consider human preferences or biases. Previously, humans used algorithms to test an existing hypothesis; now, as one investor put it, “we start with the data and look for a hypothesis”.

In financial markets as they exist today, a significant percentage of assets are either traded by computers without any human intervention or managed by them. As reported by Economist, Deutsche Bank estimates that 80% of cash-equity transactions and 90% of equity-futures transactions are carried out by algorithms. This is amazing considering the sheer volume of transactions made every day. In other words, you’d be hard pressed to find a financial institution that doesn’t use algorithms in a way that could directly impact the company’s revenue for itself and its customers.

Take inspiration from finance

As hedge funds have made clear, algorithms are capable of making certain types of decisions better and faster than humans, aided by their unparalleled ability to process and analyze data quickly. Algorithms can be applied across an enterprise to help solve any process that needs streamlining or any problem that needs effective predictive outcomes.

I understand that decision makers in many organizations feel that deep learning is too difficult to implement, especially without the help of a data scientist at hand. But the reality is that many industries are already adopting it. Ignoring this fact could result in you being left behind.

There are many options for companies to outsource the development of their algorithms and tools that allow data scientists inexperienced in deep learning to gain the experience they need through experimentation. Many companies have problems that are common enough that software vendors are already creating off-the-shelf tools that will build and run the algorithms for you on a software-as-a-service basis. Take the time to research the predictive analytics tools available to your industry and consider what options you have for plug-and-play solutions.

Algorithms have completely revolutionized the financial industry, and they are now doing the same for other industries. Far from being something to be feared, algorithms should be embraced by businesses looking to make better decisions faster and stay ahead of the competition.

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