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Applications of Artificial Intelligence

From video distribution to credit extension decisions, computers have taken over many of the jobs that used to be occupied by humans. One of the most recent advancements in the world of computers and machines is the development of machine learning, or artificial intelligence (AI).

As we continue to demand safer ways to shop online, make investment decisions and save money, banks and other financial institutions have begun to implement more and more AI into their investing products.

But AI isn’t just useful for hedge fund managers — there are plenty of ways that you can incorporate AI into your saving and investing strategies.

The best online brokerages make it simple to add an extra layer of machine-backed security to your choices. Read on to learn the past, present, and future of AI, as well as some practical applications you can use right now.

A Brief History of AI in Finance and Investing

On the most basic level terms, AI is a dynamic machine or algorithm that interprets data and learns based on the information it’s given. The term artificial intelligence comes from the primary goal of the software — to mimic the learning capabilities of human intelligence and apply them to software.

Though the concept of machine learning dates back to ancient philosophy, one of its first applications was in the world of professional checkers.

Programmers at Dartmouth College held a conference in 1956 and unveiled an astonishing advancement; a machine designed to play checkers was fed a series of checkers strategies that could accurately predict moves based on past experiences and learn how to beat human players.

The first applications of artificial intelligence would then be applied to games like Chess and Go. Today, video game AI is routine and commonplace, and the principles of artificial intelligence have expanded to the financial, medical and robotics industries.

What to Look for in AI Investing Strategy

The world of the future is here today — well, the world of AI in finance and investing is, at least. Though artificial intelligence sounds somewhat like a concept from a sci-fi movie, the truth is, you probably unknowingly interact with AI at multiple points during your investing journey.

Take credit checks and credit score calculation, for example. If you’ve ever applied for a credit card in a store or online, you may have received a decision mere minutes after submitting your application. Is a 24/7 worker sitting behind the scenes, monitoring every application that passes through?

No! Most credit decisions today are the product of algorithms and artificial intelligence. Some forward-thinking AI development companies have pushed the boundaries of credit underwriting to extend to applicants with little to no credit. For example, Los Angeles-based underwriting service ZestFinance uses thousands of data points that traditional credit bureaus don’t have access to in order to help creditors and banks gain a better picture of at-risk applicants and understand why their score is what it is.

Have you ever had a credit card company block a fraudulent transaction? You can thank AI for that. Every day, billions of credit card transactions take place, and it’s virtually impossible to manually sort out fake transactions from real ones. Cybersecurity companies like Shape Security integrate trained AI to analyze the average consumer’s spending habits and tailor the red flags that need to be triggered to block a transaction by the habits of the individual consumer.

Shape’s technology integrates thousands of data points, allowing its AI to learn about the users and detect data points outside of the norm to block financial loss. This type of artificial intelligence is a major part of the reason why if you suddenly rack up an $8,000 credit card charge, the transaction will be blocked — but if Jay-Z ran up the same charge, it would be business as usual.

Think big corporations are the only ones using AI for finance? Think again! One of the newest AI trends is within the realm of personal financial management. San Francisco-cased startup Wallet aims to shape the way we spend money by using machine learning to encourage good spending habits and discourage non-essential spending trends. Wallet aims to build machines to help consumers make smarter money decisions, especially while they’re actually out spending it.

AI’s Practical Applications in Investing

AI-Powered Portfolio Risk Assessment

Traditional stock market analysis used to focus only on specific segments of the market because there are simply too many data points generated on a minute-to-minute basis for any human or team of humans to effectively incorporate.

Luckily, AI-driven solutions can now offer more effective risk-assessment services and trading position information thanks to the ability to quickly learn and use very large sets of data.

View a company’s pricing, plus traditional and alternative data.

Yewno seeks to tackle risk evaluation from a new angle with its platform, Yewno|Edge. Yewno|Edge’s machine learning-based computational linguistics engine extracts insights on what it classifies as “concepts” – abstract and ill-defined market influencers like tariffs and data privacy.

By mapping and measuring the connections between a concept and a particular company across data sources, Yewno|Edge provides exposure scores so that users can see the quantified relationship between the two. These concept exposure scores provide users with a new framework for decision making when it comes to portfolio risk tolerance.

Yewno|Edge’s state-of-the-art technology ingests hundreds of thousands of data points on a daily basis from both fundamental and alternative sources. It compares information learned with previous information accumulated to deliver a more natural portfolio assessment.

Designed to mimic the communication and learning style of human investment analysis, Yewno|Edge provides users with access to a deep learning network that can enhance their investment research and limit their stock risks.

Read Benzinga’s full Yewno|Edge Review

Quantitative Trading Strategies

Time is money in the financial industry — and too much time spent analyzing data and manually entering data points into a formula can translate to missed opportunities.

Luckily, AI takes over a lot of this legwork. Quantitative trading is a trading strategy that uses large swaths of total market data to identify daily trading patterns and help traders make more informed decisions without spending hours manually entering data points into a formula.

AI is particularly adept at formulating quantitative strategies because stocks tend to follow patterns when it comes to daily movements. Machine learning can catalog these patterns and incorporate new data to predict if a stock or other asset will rise or fall in value.

AlpacaForcast is a Japanese deep-learning investment tool that combines AI with data storage to produce highly accurate estimates on short-term stock movements as well as long-term returns.

AlpacaForecasts's AI Prediction Matrix
AlpacaForecasts’s AI Prediction Matrix

AlpacaForecast is the result of a partnership between Alpaca AI and Bloomberg and provides traders with immediate predictions using real-time market data. Over time, Alpaca plans to store and use data points to project estimates for returns, helping traders use computer-generated buy and sell signals to enhance their trading.

Dynamic Spending Assessment and Automated Saving

If you’re a low-level passive investor, you can still take advantage of AI to help you save more and spend less.

Automated investing services link up with your bank accounts and credit card information, spend some time analyzing your spending habits and locate ways and places you can cut back.

The graphic above depicts Acorns’ moderate risk portfolio
The graphic above depicts Acorns’ moderate risk portfolio

Depending on your needs, most of these services will automatically remove funds from your account and divert them to a diversified IRA or taxable brokerage accounts. You may also manually change your information through the service platform if your situation suddenly changes and you need to reduce the amount of cash you’re saving immediately.

Acorns is a simple investing app that’s straightforward and easy enough for anyone to understand and operate. When you link your financial information, Acorns analyzes your average weekly transactions and determines how much you can reasonably afford to save each week.

The app then goes on to round up a few of your purchases each week, diverting extra cents to a custom portfolio based on your income, savings needs, and risk tolerance level.

The app claims that you won’t even notice the deductions because the AI understands how to seamlessly remove a few extra cents from your bank account with each transaction, tailoring a savings strategy to your unique needs.

Acorns is available for as little as $1 a month and the company currently has over $1 million in assets under management.

Read Benzinga’s full Acorns Review

AI-Generated Banking Advice

Most people don’t enjoy traditional banking. Driving to the bank to make deposits, waiting endlessly on the phone with customer service: It’s no surprise that you might have already turned to an online institution or branch to manage your money.

Unfortunately, the shift to online banking comes with its own set of problems, including non-responsive chatbots and outsourced customer service options.

Trim is an AI-assisted money-saving account manager. Much like Acorns, Trim analyzes your personal spending data and uses a wide range of data points to locate places you can save.

However, Trim can also go a step further, canceling services that you don’t use often, negotiating bills with creditors and can even use your personal data to gather quotes for home and auto insurance in your area, compare them to what you’re currently paying and recommend alternate options.

Dynamic Tax Assistance

For most Americans, tax season is a dreadful period spent endlessly poring over receipts and IRS forms, desperately trying to minimize liability while also taking advantage of every possible credit and deduction.

Add in the fact that the U.S. tax code has become so complex that the average American cannot reasonably be expected to read and understand it in its entirety, and you have a situation that’s ripe for AI intervention.

As the most popular tax preparation software in the country, TurboTax is in the unique position of having access to hundreds of thousands of data sets previously only available to the IRS.

In 2018, TurboTax announced that newer versions of its software would begin to incorporate an AI focus on tax prep to help users maximize their deductions and credits and make suggestions on how to prepare their form.

For example, let’s say you’re a florist and you’re filing taxes on behalf of your business. TurboTax will average together anonymous data from thousands of other florists who filed their taxes last year and use that data to suggest deductions and make estimates of how much you can expect to get in your return. TurboTax can also save data from year to year, helping you streamline your tax filing process and save more money every year.

Final Thoughts

So much financial data has become integrated and automated that you might be concerned that cybersecurity will also rise in the coming decade. If you have a brokerage account, credit card or bank account linked to an AI-assisted data provider or service, it’s a good idea to keep an eye on your credit to make sure that you don’t become the victim of identity theft if there is a data breach.

Turn on account security factors like two-factor authentication for all of your accounts to help ensure that you are the only person who has access to your data.

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