5 Applications of Machine Learning in Finance

10/18/2019

5 Applications of Machine Learning in Finance - USM systems - Medium

5 Applications of Machine Learning in Finance USM systems Oct 18 ¡ 4 min read

In particular, ML services hold great promise for companies in the financial sector.

Whether they offer consumer loans or invest funds in the market, financial institutions want to make smarter decisions about how to allocate their capital. This means that these businesses need as much information as possible about their clients to better predict their future return on investment.

By processing and analyzing large volumes of data, machine learning software improves the capabilities of financial institutions, making it impossible for even a team of experienced analysts. In this article, we will discuss five important ways that machine learning can change the face of finance. https://medium.com/@fugenxmobiledevelopment/5-applications-of-machine-learning-in-finance-3e2286b8f0

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10/18/2019

5 Applications of Machine Learning in Finance - USM systems - Medium

1. Portfolio Management Traditionally, human portfolio managers run vehicles such as ETFs, mutual funds and hedge funds that invest your money in return for a given percentage of revenue.

However, the “dirty little secret” of investing is that most professional portfolio managers can’t beat standard market indices like the S&P 500.

“Robo-Advisors” provide you with the opportunity to save money by essentially getting a marketable return while optimizing your assets to satisfy the level of risk you are comfortable with. Through Artificial Intelligence and Machine Learning, automated software agents can use historical results to assess the best way to allocate your investments.

Because robotic portfolio managers are fully driven by algorithms, there is no need to charge high fees to pay one’s salary, with little or no human input. Not only do Roboadvisors manage your portfolio, but they can also perform arbitration tasks such as automatically generating reports and preparing for audits. Thanks to being largely selfsufficient, robot-advisors can handle huge portfolios — some as big as $ 100 billion — at virtually no cost.

2. Algorithmic trading A special application of Robo-Advisors is algorithmic trading: using high-powered hardware and software to buy and sell assets faster. Algorithmic trading is almost always impossible for human traders to display — they do not have the brainpower to analyze the huge amounts of data they need to read every second in order to profit.

Hedge funds that use algorithmic trading are often referred to as “quantitative hedge funds” or “black box hedge funds”. These companies use sophisticated computer programs powered by machine learning and artificial intelligence to trade personal securities with high speed and frequency.

3. Fraud detection With data breaches becoming a common occurrence, fraud has become a major problem for banks and credit card companies. U.S. Consumers lost $ 16 billion in fraud and identity theft in 2016. https://medium.com/@fugenxmobiledevelopment/5-applications-of-machine-learning-in-finance-3e2286b8f0

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10/18/2019

5 Applications of Machine Learning in Finance - USM systems - Medium

With hundreds of millions of bank accounts and credit cards, it is impossible to manually find cases of fraud. The only way that financial institutions can hope to fight fraud is to use machine learning algorithms to detect exceptional transactions.

By giving the algorithm millions of data points about real and fraudulent operations, machine learning models can give better estimates of what transactions are suspicious.

Some common methods of detecting fraud are to find amounts, locations, and times that are not common.

4. Loans and insurance underwriting Banks and credit card companies traditionally use only basic heuristics about their customers when making financial decisions. First, the human analyst can view half a dozen pieces of information, such as the customer’s age, credit score, position, and occupation. The company then decides whether to give a loan or open a new credit card account and if so, with what rates and terms.

In today’s world, financial institutions get more information about their customers than any human can retain — probably hundreds of thousands of data points per person.

Only machine learning algorithms can process this information and help companies make decisions that increase their profitability.

Similarly, insurance underwriters may want to limit the amount of money an insurance company pays to its policyholders. Now available companies with life insurance, health insurance, property insurance, disaster insurance, and more, need AI and machine learning models to use all the information about their customers at their fingertips.

5. Digital Assistants You may have heard of Apple’s Siri and Amazon’s Alexa, but digital assistants are also invading the world of financial services. Automated phone systems that rely on machine learning can help guide callers to the right segment of the company, delivering goodquality customer service without the need for human employees.

AI and machine learning techniques are used for speech recognition and natural language processing to help customers understand what they want and connect with a

https://medium.com/@fugenxmobiledevelopment/5-applications-of-machine-learning-in-finance-3e2286b8f0

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10/18/2019

5 Applications of Machine Learning in Finance - USM systems - Medium

human agent if needed. For example, repetitive neural networks (RNNs) are used to parse the voice clip’s personal phonemes and assemble them into words.

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