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

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 do 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. Yes, artificial intelligence in finance and machine learning in finance are outstanding opportunities and our inevitable future. How so?

artificial intelligence in finance and machine learning in finance

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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 artificial intelligence play a key role in today’s finance industry. Let’s examine some of the recent machine learning applications in finance.


We start our list of machine learning use cases with something that’s crucial for every investor. 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].

Another example 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. Their robo-advisor is called SoFi Wealth (currently also Automated Investing[3]). SoFi Wealth 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.



This is another vital example of artificial intelligence in finance. In Machine Learning, issues like fraud detection are usually framed as classification problems. As you probably know from one of our recent articles, classification is a method that estimates the probability of an occurrence of a given event based on one or more inputs. In the finance sector, it involves creating models that have enough compute 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.

According to The 2018 Global Fraud and Identity Report[5] 63% of businesses have experienced the same or more fraud losses in the past 12 months (2017/2018). As other research states, criminals successfully stole a whopping amount of £1.2 billion through fraud and scams in 2018[6]. And in the UK only!

FRAUD DETECTION, magnifier, euro


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 artificial intelligence, HFT would never be possible, as it requires a pace of work far beyond human limits. 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. They have supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis[7].

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. They have been so successful that most of the High-Frequency Trading companies have integrated such models in their trading modules.



This machine learning use case is related chiefly to loans and mortgages. Naturally, banks and insurance companies have access to thousands of gigabytes of consumer data. They know pretty much everything about us. What is our income, what are our expenses, where do we eat, where do we buy clothes, which telecom services we use and so on. And ML algorithms can be trained on this knowledge! The machine learning algorithms can perform automated tasks like matching data records, looking for exceptions, and, what’s particularly interesting, calculating whether an applicant qualifies for a loan or insurance. Yes, all these time-consuming credit-scoring tasks can be easily conducted with the usage of artificial intelligence! Not only is it much quicker but, on many occasions, more accurate as well!

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. This means that these applications can predict very accurately an applicant’s ability to pay back their loan.

RISK MANAGEMENT, finance, people, calculations

This brings us to the final question.

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Why Should Finance Professionals Learn AI And Machine Learning?

Machine learning starts to play a major role in the finance sector, and the predictions are univocal–every year we will see more and more machine learning use cases. AI and machine learning are the next great milestones in the history of finance. That’s why it is crucial for finance professionals to master AI. To understand how they can benefit from it, and what results they ought to expect from AI professionals.

When knowledge of AI in finance and machine learning 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. Machine learning finds various applications within this sector, and it really is just a question of time when it becomes a worldwide standard.


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

If you find this topic interesting, try our publication about Automated Machine Learning.


[1] Nikhil Gokhale, Ankur Gajjaria, Rob Kaye, Dave Kuder. AI leaders in financial services. Aug 13, 2019. URL: https://www2.deloitte.com/us/en/insights/industry/financial-services/artificial-intelligence-ai-financial-services-frontrunners.html. Accessed Feb 6, 2020.

[2] Ayn de Jesus. Robo-advisors and Artificial Intelligence – Comparing 5 Current Apps. Nov 24, 2019. URL: https://emerj.com/ai-application-comparisons/robo-advisors-artificial-intelligence-comparing-5-current-apps/. Accessed Feb 6, 2020.

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

[4] Rafael Pierre. Detecting Financial Fraud Using Machine Learning: Winning the War Against Imbalanced Data. June 27, 2018. URL: https://mlopshowto.com/detecting-financial-fraud-using-machine-learning-three-ways-of-winning-the-war-against-imbalanced-a03f8815cce9. Accessed Feb 6, 2020.

[5] Experian. The 2018 Global Fraud and Identity Report. URL: https://www.experian.com/assets/decision-analytics/reports/global-fraud-report-2018.pdf. Accessed Feb 6, 2020.

[6] UKFinance. Fraud the facts 2019. URL: https://www.ukfinance.org.uk/system/files/Fraud%20The%20Facts%202019%20-%20FINAL%20ONLINE.pdf. Accessed Feb 6, 2020.

[7] Wikipedia. Support-vector machine. URL: https://en.wikipedia.org/wiki/Support-vector_machine. Accessed Feb 6, 2020.

[8] Daniel Faggella. Machine Learning for Underwriting and Credit Scoring – Current Possibilities. Apr 3, 2020. URL: https://emerj.com/partner-content/machine-learning-underwriting-credit-scoring/. Accessed Feb 6, 2020.

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