Let’s talk today about machine learning and AI in finance. Fraud detection, high-frequency trading, risk management, investment management–this list goes on and on. What do these disciplines have in common? All of them are finance-related, and all of them can be transformed thanks to AI and Machine Learning in finance. And this is exactly what we’re going to do in this article–we will examine machine learning and AI applications in the finance sector.

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Until recently, finance was full of human labor. Each credit application, each share purchase has been made by hand or with little computer assistance. Just a few years ago, if you wanted to buy or sell stocks, you had to call your bank or broker and tell them to do it for you. With the advent of the Internet in the 1980s, things started to shift. More and more financial processes were done via the Internet. Today, all of us are used to doing almost everything online. When was the last time you had to go to your bank? Probably months ago.

The Internet has brought a huge shift in the way we sell, purchase, and exchange money. However, as we have the web for quite a bit of time now, the time has come for another major revolution. And this revolution is dominated by four letters–AI and ML in finance. Yes, machine learning and AI in finance are outstanding opportunities and our inevitable future. How so?

artificial intelligence in finance and machine learning in finance

You may also find it interesting – Data Science in Finance

How Are AI And Machine Learning Changing Finance?

Both these disciplines can be immeasurably beneficial, either to the financial corporations and to you–their customer. Long story short–artificial intelligence transforms the entire sector to make it work more efficiently and quicker. With the aid of the finance machine learning applications, you will get your mortgage within one day, obtain precise investment advisory, and trade more efficiently on the stock exchange.

On the other hand, financial companies will be able to detect fraud attempts and manage risk more accurately, which, eventually, translates into better customer service and safety. But how exactly can artificial intelligence change the finance sector? Let’s examine some of the essential machine learning applications in finance and AI use cases.

How Are AI And Machine Learning Changing Finance?

How Does AI Work In Finance?

According to a study conducted by the consulting company Deloitte[1], most sector leaders have started exploring the use of AI for various reasons, chiefly for revenue enhancements and client experience initiatives. Moreover, they have applied metrics to track their progress. As a result:

  • Leading financial services firms are achieving companywide revenue growth of 19% directly attributable to their AI initiatives
  • 60% of frontrunner financial services firms are defining AI success by improvements to revenue and 47%, by improving customer experience
  • 45% of AI frontrunner firms are investing over 5 million USD in AI initiatives today, 3x the level of starters or late adopters

According to the same study, companies seek cost reduction, revenue enhancement, and customer engagement. Therefore, the vast majority of their initiatives revolve around the aforementioned three goals. And they are willing to achieve them through various ways and tools. The leading financial companies surveyed by Deloitte invest i. a. in:

  • AI as a service (56%)
  • Enterprise software with integrated AI (61%)
  • Data science modeling tools (54%)
  • Automated machine learning (63%)
  • Open-source AI (65%)

How Does AI Work In Finance?, board

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How Is Machine Learning Used In Finance?

Machine learning and AI play a key role in today’s finance industry. Let’s examine some of the recent ML applications in finance.

Investment Portfolio Optimization

We start our list with use cases of machine learning in finance. Currently, investment companies are working on machine learning applications, which can serve as an assistant to every investor. Betterment is such an online investment company. They have devised robo-advisors to provide financial advice or portfolio management services. Everything happens without human assistance! Betterment’s robot invests and manages individual, IRA, and ROTH IRA accounts for their users[2].

SoFi: machine learning in finance

Another example of machine learning and AI in finance derives from SoFi. SoFi originally started as a lending company, primarily for student loans, but has expanded to other finance fields, to name just mortgage financing and personal loans. Moreover, SoFi invests and manages customers’ money in ETFs, helps individuals invest for retirement in instruments such as traditional deductible IRA accounts, simplified employee pensions, and ROTH IRAs.

INVESTMENT PORTFOLIO OPTIMIZATION, charts, hands

Fraud Detection

This is another vital example of AI and ML in finance. In Machine Learning, issues like fraud detection are usually framed as classification problems. In the finance sector, it involves creating models that have enough computer power to correctly classify transactions as either legit or fraudulent, based on transaction details such as amount, merchant, location, time and others[4].

ML systems can scan through these vast customers’ datasets, detect unusual activities (anomalies), and flag them instantly. This, in turn, allows human employees to check this unusual activity much faster and verify it with a customer. This technology has everything it takes to become a game-changer, as frauds are a serious problem.
FRAUD DETECTION, magnifier, euro

High-Frequency Trading

As its name indicates, HFT comprises hundreds of thousands of trades per day. Today, they are executed by sophisticated machine learning finance algorithms. Thanks to these algorithms, it is possible to benefit from price differences that might exist only for a fraction of a second. Without ML in finance, HFT would never be possible, as it requires a pace of work far beyond human limits.

SVM models

There have been a number of machine learning algorithms applied in this field. The most common of them are SVMs. This abbreviation stands for Support Vector Machines. The SVM models are trained to recognize features that indicate an upcoming increase or decrease in the market pricing and bid accordingly within a fraction of a second. Furthermore, they have been so successful that most of the High-Frequency Trading companies have integrated such models in their trading modules.

HIGH-FREQUENCY TRADING, charts

Risk management

This use case of machine learning and AI in finance is related chiefly to loans and mortgages. The machine learning algorithms can perform automated tasks like matching data records and looking for exceptions. In addition, algorithms can calculate whether an applicant qualifies for a loan or insurance.

ZestFinance: risk management system in action

One of the companies that implement such a risk management system is ZestFinance[8]. The risk management models they put into production have thousands of variables to estimate the risk level for each customer. Their system is aimed at checking whether a given loan applicant is risky. And the fact is, with machine learning, the number of data sources that a machine learning application can factor into a credit model are theoretically infinite. Therefore, this means that these applications can predict very accurately an applicant’s ability to pay back their loan.

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Trade Settlements

Despite the fact that the overwhelming majority of transactions are calculated automatically and with minimal human participation, about 30% of transactions fail, and they have to be calculated manually.
But using machine learning and AI in finance can help determine the cause of failed trades and analyze why trades were rejected. In addition, machine learning algorithms can even provide a solution and predict which trades might fail in the future.

SmartChaser in BNP Paribas

BNP Paribas launched the system Smart Chaser that predicts unsuccessful machine learning operations in advance. Also, Smart Chaser applies predictive analytics for operations that may be problematic and require intervention. The algorithm can determine which operations are most likely to fail, the reasons for that failure, and possible solutions to the problem. Consequently, the system develops the most efficient use of time for banking teams. It’s a perfect example of how machine learning and AI can be implemented in finance companies.

Why Should Finance Professionals Learn AI And Machine Learning?

Machine learning and AI in finance are starting to play a major role, and the predictions are univocal–every year we will see more and more AI and ML use cases in finance. Moreover, machine learning and AI are the next great milestones in the history of finance. That’s why it is crucial for professionals to master AI and ML in finance. When knowledge of machine learning and AI in finance becomes widespread, which, hopefully, will happen just within two-three coming years, the entire sector will be much more efficient, well-organized, and much safer.
finance

Key Takeaways

  • Machine learning and AI in finance can be immeasurably beneficial, either to the financial corporations and to their customers.
  • AI and ML in finance transform the sector to make it work more efficiently and quicker.
  • The main applications of machine learning and AI in finance: investment portfolio optimization, fraud detection, high-frequency trading, risk management.
  • Knowledge of machine learning and AI in finance has become widespread and as a result just within two-three coming years, the finance sector will be much more efficient, well-organized, and much safer.

You can be a part of this revolution! And this is why we are here. To help you implement finance machine learning and AI into your business. Don’t feel discouraged to reach us, even if you are at the very beginning of this trip, Addepto is an experienced machine learning consulting company. We will guide you through the intricacies of AI. So, let’s talk!

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References

[1] Deloitte.com. AI leaders in financial services. URL: https://www2.deloitte.com/us/en/insights/industry/financial-services/artificial-intelligence-ai-financial-services-frontrunners.html. Accessed Feb 6, 2020.

[2] Emerj.com. Robo-advisors and Artificial Intelligence – Comparing 5 Current Apps. URL: https://emerj.com/ai-application-comparisons/robo-advisors-artificial-intelligence-comparing-5-current-apps/. Accessed Feb 6, 2020.

[3] Sofi.com. Automated investing. URL: https://www.sofi.com/invest/automated/. Accessed Feb 6, 2020.

[4] Mlopshowto.com. Detecting Financial Fraud Using Machine Learning: Winning the War Against Imbalanced Data. URL: https://mlopshowto.com/detecting-financial-fraud-using-machine-learning-three-ways-of-winning-the-war-against-imbalanced-a03f8815cce9. Accessed Feb 6, 2020.

[5] Algorithmxlab.com. 10 Applications of Machine Learning in Finance. URL:https://algorithmxlab.com/blog/applications-machine-learning-finance/#Document_Analysis. Accessed July 30, 2021.

 

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