With the onset of the digital age, businesses across practically every industry gained access to massive troves of previously unavailable data about the behavior and habits of their customers. These data, in combination with modern analytical techniques, have allowed businesses to gain new insights and improve decision-making processes in ways that would have once been unimaginable. Among the most important of the analytical tools that have made this revolution possible is machine learning. Here’s what you should know about the role of big data and machine learning in the world of finance and how institutions the world over are using data to their advantage.
How Machine Learning and Big Data Work Together
To fully understand how machine learning and big data apply to the financial sector, it’s important to first understand how the two technologies work togetherOpens a new window . Machine learning, being a method for allowing computational systems to “learn†through analyzing existing data to build and improve algorithms, becomes more effective as more and more data are made available. When big dataOpens a new window , the cumulative sum of all relevant data available to a given organization, is given to a machine learning system, that system can become vastly better at making predictions and identifying important patterns.
Finance and Machine Learning
As a heavily data-driven business sector, the finance industry has been on the cutting edge of machine learning for many years. One of the many uses of combining machine learning and big data in the finance world is the detection of fraudulent financial activity.
Each year, banks lose some $6 billionOpens a new window due to synthetic identity fraud, while the yearly cost of credit card fraud is expected to balloon to $35 billion by 2020Opens a new window . By analyzing the data from immense numbers of transactions, however, machine learning algorithms can detect anomalous activity Opens a new window that could indicate fraud. This type of detection still requires human investigation to confirm fraudulent activity, but it can be an extremely useful tool in identifying potential risks and making them known to the correct professionals.
Another trend that has radically altered the finance world is the development of so-called robo-advisors that use machine learning to guide customers’ investment decisions. Robo-advisors, which now account for $140 billion in assetsOpens a new window , use information given to them by users to estimate risk tolerance and suggest investments based on the users’ financial goals. By automating the investment process, robo-advisors allow users to pay extremely low fees while taking passive roles in the ways their money gets invested. These characteristics have made robo-advisors quite popular among younger investorsOpens a new window .
Among the most exciting and important uses of machine learning in the financial sector is the growing ability of computer algorithms to make accurate stock predictions. By analyzing vastly more data than even the most astute human investor could ever hope to process, machine learning algorithms can predictOpens a new window likely lows, highs and even opening and closing prices for specific stocks. Computer scientists are also currently researching new methodsOpens a new window that would allow algorithms to predict the price movements of stocks over longer periods of time.
Finally, businesses in the financial sector have employed machine learning and big data to predict customer behavior and market products and servicesOpens a new window accordingly. Machine learning can be used to more effectively target offers, optimize pricing models and even power chatbots that can improve the customer experience. Using these methods, financial institutions can improve their profits and customer conversion rates.
How Well Has Implementing Machine Learning in Finance Gone?
As with any new technology, the implementation of machine learning in finance has seen both successes and drawbacks. Among the most prominent successes have been those involved in predictive trading. In 2017, hedge funds that used AI or machine learning to make trading decisions outperformedOpens a new window more traditionally managed funds. Also pointing to the successful implementation of machine learning is the fact that the robo-advisors that have been made possible by the technology are expected to grow to manage over $1 trillion in assetsOpens a new window by the year 2020, and as much as $4.6 trillion by 2022.
At the same time, though, there are still very real challenges in the implementation of machine learning. One notable difficulty is the fact that machine learning algorithms do not seem to be equally proficient in all tasks, even if those tasks are broadly similar. An interesting example of this problem is the fact that common machine learning programs are far more effective at trading U.S. Treasury bonds than they are at trading options. This has resulted in a massive push to find the best machine learning tools for every given application that is still taking place.
Another problem that applies to machine learning and big data in general is that big data isn’t necessarily good data. When swamped with mountains of irrelevant or low-quality data, machine learning algorithms aren’t able to produce good predictive models. This is known as the “garbage-in, garbage-outâ€Opens a new window problem. With businesses of all sorts collecting more data than they generally can use, there is a risk of sacrificing the quality of data in favor of quantity, thus hindering the potential benefits of the machine learning algorithms that depend on those data.
Overall, the combination of machine learning with big data has produced immense benefits in the world of finance. As more and better data are compiled and as machine learning tools continue to improve, these benefits will likely continue to grow. At the same time, though, there are still challenges to be solved through further research and development of these powerful tools.