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Robotizing investments using machine learning

It used to be that investment firms relied completely on highly educated and highly qualified financial professionals to compile financial data, parse its meaning, and use those interpretations to drive investment decisions. While this way of doing things often led to stable investments, it also had a definite flaw: it took too much time. Financial professionals spent most of their time drowning in data and not enough time working directly with customers, assessing risks, or handling other responsibilities. The system was, in a word, inefficient.

Robotizing the investments with machine learning

In recent years, machine learning has presented itself as a potential solution to the inefficiency problem in the finance world. By robotizing their investments with machine learning, financial service providers have been able to locate better investments, manage financial risk, spot fraud, and serve customers more efficiently — among other benefits.

Also read: The Fundamentals of Machine Learning [whitepaper]


What Is Robotizing?

The term “robotizing” calls to mind an image worthy of a sci-fi film — with robots answering calls from clients, selling stocks, managing portfolios, and more. In reality, “robotizing” doesn’t refer to actual robots. Rather, the term has mostly to do with algorithms, which are at the heart of machine learning and all the benefits that it has to offer the financial industry.

Portfolio management

One of the clearest examples of robotizing taking root in finance is in the area of portfolio management. These days, with so many people saving money for retirement, houses, their kids’ college funds, and other assorted purposes, many firms simply have too many clients to interact with them all on an email or telephone basis — let alone face-to-face.

That’s where machine learning — and “robotizing” — comes into play. Financial service providers are implementing “robo-advisors” to handle the portfolio management side of things for many users. What usually happens is that a user will create an account and start a portfolio by visiting a financial service company’s website. The users then fill out a form, entering personal details (age, income, etc.), setting financial goals, and providing details about their existing financial assets.

Instead of sending this information to a flesh-and-blood financial advisor for review and analysis, the website uses a robo-advisor to process the user’s account details and create an investment plan. The sophisticated algorithm considers not only the user’s financial information but also current details concerning the health of the finance market and information about the user’s tolerance for risk. The algorithm can process this information faster than any human ever could, creating a personalized investment strategy for each user on a level that would simply be impossible without robotized technology.

The Customer Service Question

If there is a weakness to this type of robotization, it’s that it runs the risk of making “lower-value” users feel like they are being neglected by financial service providers. In general, millennials — who are starting to invest more heavily as they settle into the job market, buy homes, and start families — don’t expect the same kind of person-to-person interaction in their transactions as previous generations. As a result, more and more investors are willing to accept the robo-advisor model, rather than demanding that their financial service providers assign them a human investment advisor.

Still, when users have questions, concerns, or thoughts that need clarifying, they are going to want to speak with someone. Machine learning and robotization can simplify this side of things, too. Many fintech firms are now using algorithms to drive their online “live chat” systems or Q&A rooms. These systems can analyze customer questions and deliver helpful answers to most queries. In other words, users get quality customer service with minimal demand on the time of human financial advisors.

Creating Smarter Financial Predictions with Machine Learning

In addition to providing each user with their own “robo-advisor,” machine learning also helps financial service companies predict the potential financial outcomes of different investment strategies. There are sophisticated machine technologies out there that can process the feeds from multiple funds at once, aggregate them together, and use the information to make predictions about the likely directions of different stocks.

As a result, financial service providers have a better idea of when to trade and when not to trade, all thanks to robotization. This predictive edge helps financial professionals be more accurate when managing investments, which in turn usually means higher returns.

The future of the finance world

Robotization is not for every investment firm. Machine learning, while it is becoming more common — particularly in this industry — is still quite expensive. However, for how robotization improves efficiency, allows for better customer service, and offers more accurate financial predictions, it is almost sure to be the future of the finance world.

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Also read: The Fundamentals of Machine Learning [whitepaper]

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Bram Nawijn

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