Reading Time: 6 minutes

How Machine Learning in Finance Changes the industry: Modern Realities and Future Forecasts


2.5 thousand years ago, the philosopher-mystic Pythagoras claimed that everything can be expressed in numbers. At that time, no one understood him. Today we are witnessing a digital breakthrough when machines analyze large amounts of data on decisions made by people in different situations, translate learning algorithms into their own language and act by analogy with humans.


Today, developments in the field of AI confidently follow the path of creating a computer, the cognitive functions of which will not be inferior to the human brain. The finance industry is one of the most promising areas to apply these technologies.


The Future of AI in Finance


Machine learning in focus: How is it used in Financial Services


Machine learning in finance is rapidly developing – there are already dozens of options for its use in the financial sector.


Credit Solvency Assessment


Artificial Intelligence helps banks to more confidently credit those who pass system checks. For this, programs and algorithms analyze all available information about a potential borrower, study the credit history, changes in the level of wages, and on this basis determine the reliability of the client and the security of the loan. Moreover, Chinese banks have already gone further and decided not to limit themselves to analyzing the data exclusively.


They began to introduce facial microexpressions recognition technology. This allows them to find out if customers are lying about their financial situation when they come to take loans. To do this, they developed AI systems that, with the help of smartphone cameras, detect minimal changes in facial expressions that are invisible to the naked eye. Thus, banks identify potential fraudsters, and they have already reduced their losses from unpaid loans by 60%.



This is a global task that is successfully solved through the introduction of Artificial Intelligence. When the algorithm is able to analyze all the variety of structured and unstructured data (both internal, from the company’s business processes and external, such as customer requests and their actions in social media), a financial institution can discover and use both useful and potentially dangerous trends. It helps to assess risk levels and make the most informed decisions.



Read more on Machine Learning in Enterprise



Fraud Protection


Now banks and payment systems are already developing models to identify and block most fraudulent transactions. These models are built on the client’s transaction history, as well as on the client’s behavior on the Internet. Systems based on Artificial Intelligence, allowing to detect online frauds are developed on these Big Data technologies.


Fraudulent social engineering will also be reduced by Artificial Intelligence. For example, when an impostor pretending to be a bank employee fakes data, his activity will be neutralized. Such systems will make financial deception unprofitable for criminals, and most felonious schemes will “die”.


Service Level Improvement


Many banks have implemented AI-based applications that allow customers to get answers to current questions. For example, a client can find out the expenses this month, the amount spent on food, credit card debt, the type of the most profitable insurance, and etc.


There are applications that, when connected to the payment system, analyze accounts, for example, for mobile communication or the Internet, and offer the owner more advantageous tariffs. Sophisticated algorithms analyze user behavior online and allow financial institutions to develop more personalized and mutually beneficial offers. For example, if a customer is looking for opportunities to buy a car, the bank, having this information and analyzing his/her solvency, can develop a suitable loan offer.


Customer Retention and Acquisition Based on Data Analysis


Based on the analysis of the individual financial behavior of a client, banks are developing appropriate advertising or proposals. In this way, banks also receive information about the intentions of a customer or a potential customer. They get the opportunity to respond in time and take measures to withhold if the client plans to refuse to work with this bank or to attract a new client who currently needs a personalized offer.



Read more on AI in Customer Service



Increase Efficiency


Systems with AI help automate and optimize processes, occurring in bank branches. In the future, it is planned to completely abandon the use of paper media. All information will exist in an electronic form. Thus, AI can facilitate the work with internal operations, taking on routine operations with which it is able to handle many times more efficiently and faster than any competent employee.


In addition, the ability of an AI to collect and structure constantly updated information can increase the efficiency of reporting, while designing the internal documentation, and even a list of frequently asked questions, which will be continuously adjusted in accordance with the latest updates.


Development of Investment Strategies


Each time, processing a new flow of information, Artificial Intelligence learns and systematizes its knowledge. This allows it to assess the situation on the market and form the most profitable investment transactions via Big Data analytics. Many hedge funds have already made algorithmic trading their trump card. Apart from the fact that the program is able to study, analyze and systematize the volume of data that is immense for a human brain, the main advantage of using AI in the development of investment strategies is the fact that it knows nothing about typical human feelings and emotions. Greed, fear, and excitement are alien to this technology. Therefore, all its assumptions are absolutely rational.


Collection of User Data in the Context of Ethics


All these tasks are solved successfully with the help of Big Data analytics, including personal data, and Artificial Intelligence obviously succeeded in this. However, it will be reasonable to ask a question about the ethics of collecting such information about users. At the moment, only 58% of users are calm about collecting information about themselves. But this is true that only if there is trust between a user and the other side, their cooperation is really mutually beneficial, and customers can be sure of the safety of their personal data.

AI Solutions for Finance


Hidden Dangers of AI in the Financial industry


Experts in the field of AI development recognize that humanity will still live to see the emergence of real Artificial Intelligence, which will be smarter than its creator. And most importantly, such a system created with Artificial Neural Networks will think and make decisions independently. The first and most obvious consequence of this innovation is that the financial industry (as well as other major sectors) will begin to gradually abandon the participation of people in business processes.


Societe Generale, one of the largest banks in France, has already announced a reduction of 15% of its branches and 900 workplaces by 2020 as a part of cost reduction measures due to the more active introduction of digital technologies. Thus, the employees of the financial industry that perform routine work will soon be replaced by Artificial Intelligence.



Read more on AI in Retail



Leading Japanese companies have also declared their readiness to automate more than 30 thousand workplaces. The leaders of the banks concluded that this was a necessary measure since the traditional methods of doing business did not help to increase profits. Japanese bankers have come to the conclusion that decisions from the field of Artificial Intelligence will minimize the costs of human labor.


At the same time, the introduction of AI is not a remedy for all possible errors, although, this algorithm cannot make the same error twice.

Investments and Perspectives of AI Introduction into the Financial industry


However, despite all the dangers and drawbacks, the statistics prove that sophisticated algorithms cope well with their tasks and increase profits of those who already use it for business.


  • 46% of fintech companies consider it necessary to invest in AI during the next 12 month.
  • 25% of banks are currently working on the development of systems that will be able to prevent fraud. Moreover, this opportunity is considered by financial institutions as the most important.
  • According to forecasts, banks will be able to save $1 trillion by 2030 with the help of AI.
  • The greatest amount of investments in AI will be made by companies from the financial industry.



Definitely, Artificial Intelligence today is a promising area for investment. Companies that work in the field of finance will be able to remain competitive in the coming decades if they pay attention to these innovations today. Introducing Machine learning algorithms and Artificial Intelligence in the financial industry, they need to consider a system of protection against cyber attacks and give users confidence in the safety of their data first of all.


If you are interested in Machine learning development for your business – feel free to reach us for a personal consultation!