5 Applications of Machine Learning in Finance


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Machine learning is transforming the face of nearly every industry. In 2017, a majority of organizations—51 percent—finally reported that they were using artificial intelligence to improve their business processes.

In particular, machine learning holds a great deal of promise for companies in the financial sector. No matter whether they provide consumer loans or invest funds in the market, financial companies want to make the smartest decisions about how to allocate their capital. This means that these businesses need as much information as possible about their clients in order to better predict future returns on their investment.

By processing and analyzing massive quantities of data, machine learning software enhances financial companies’ capabilities, performing tasks that are impossible for even a seasoned team of analysts. In this article, we’ll discuss five of the most important ways that machine learning is transforming the face of finance.

1. Portfolio Management

Traditionally, human portfolio managers have run vehicles such as ETFs, mutual funds, and hedge funds that invest your money in exchange for a given percentage of the returns. However, the “dirty little secret” of investing is that even most professional portfolio managers can’t beat standard market indexes such as the S&P 500.

“Robo-advisors” offer the chance to save money by getting returns that essentially match the market while fine-tuning your assets in a way that satisfies the level of risk that you’re comfortable with. Through artificial intelligence and machine learning, automated software agents can use historical results to estimate the best way to allocate your investments.

Because robotic portfolio managers are driven completely by algorithms, with little or no human input, there’s no need to charge higher fees in order to pay someone’s salary. Not only can robo-advisors manage your portfolio, they can also automatically perform intermediary tasks such as generating reports and preparing for audits. Thanks to being largely self-sustaining, robo-advisors can handle massive portfolios—some as big as $100 billion—at virtually no cost.

2. Algorithmic Trading

One particular application of robo-advisors is to perform algorithmic trading: the use of high-powered hardware and software to rapidly buy and sell assets. Algorithmic trading is almost always impossible for human traders to perform—they simply don’t have the brainpower to analyze all the massive quantities of data they need to read every second in order to turn a profit.

Hedge funds that use algorithmic trading are often called “quantitative hedge funds” or “black box hedge funds.” These companies use sophisticated computer programs, driven by machine learning and artificial intelligence, to trade individual securities at very high speeds and frequencies.

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3. Fraud Detection

As data breaches become a more common occurrence, fraud has become a major problem for banks and credit card companies. U.S. consumers lost an estimated $16 billion to fraud and identity theft in 2016.

With hundreds of millions of bank accounts and credit cards, trying to find instances of fraud manually is plainly impossible. The only way that financial companies have any hope of fighting fraud is to use machine learning algorithms to detect unusual transactions.

By feeding the algorithm millions of data points about real and fraudulent activities, machine learning models can make better guesses about which transactions are most likely to be suspicious. Some of the most common methods of detecting fraud are identifying amounts, locations, and times that are out of the ordinary.

4. Loans and Insurance Underwriting

Banks and credit card companies have traditionally used only basic heuristics about their customers when making financial decisions. First, a human analyst might look at half a dozen pieces of information such as the customer’s age, credit score, location, and occupation. The company then decides whether or not to provide a loan or open a new credit card account, and if so with what rates and terms.

In today’s world, however, financial companies have access to more information about their customers than any human being could possibly retain—perhaps hundreds of thousands of data points for each person. Only machine learning algorithms are able to process all of this information and use it to help companies make decisions that maximize their profitability.

Similarly, insurance underwriters want to limit the amount of money that an insurance company will have to pay out to its policyholders. With so many different kinds of insurance now available—life insurance, health insurance, property insurance, disaster insurance, and more—companies need AI and machine learning models to use all the information about their customers that’s at their fingertips.

5. Digital Assistants

You’ve heard of Apple’s Siri and Amazon’s Alexa, but digital assistants are invading the world of financial services as well. Automated phone systems that rely on machine learning can help route callers to the right department within a company, providing good-quality customer service without the need for human employees.

AI and machine learning techniques are used for speech recognition and natural language processing in order to understand what customers want and connect them with a human agent if necessary. For example, recurrent neural networks (RNNs) are used to parse the individual phonemes of a voice clip and assemble them into words.