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Machine Learning in Banking – Opportunities, Risks, Use Cases

To get a defining advantage over the competitors the banks, as well as other financial institutions must dig deep into technology breakthroughs. Luckily, Artificial Intelligence and Machine Learning are already here to help them to achieve it. Improving data processing will lead to better strategies and fraud prevention levels.

Artificial Intelligence in Banking Statistics

  • According to a forecast by the research company Autonomous Next, banks around the world will be able to reduce costs by 22% by 2030 through using artificial intelligence technologies. Savings could reach $1 trillion.
  • Financial companies employ 60% of all professionals who have the skills to create AI systems.
  • It is expected that face recognition technology will be used in the banking sector to prevent credit card fraud. Face recognition technology will increase its annual revenue growth rate by over 20% in 2020.

How Artificial Intelligence is Used in Banking

The data that banks receive from their customers, investors, partners, and contractors is dynamic and can be used for different purposes, depending on which parameters are used to analyze them. Basically, the scope of AI for banking can be grouped into four large groups.

Improving Customer Experience

When banks and other financial organizations got the opportunity to learn everything about a user and his behavior on a network, they simultaneously gained the opportunity to improve the user experience as much as possible.

Chatbots

For example, if a user has difficulty working with a website or application, chatbots are used to lead him along the right path and at the same time reduce bank support staff’s workload. In addition, modern chatbots can perform simple operations such as locking and unlocking cards as well as send notifications to the user if he has exceeded the overdraft limit — or vice versa if the account balance is higher than usual.

Personalized Offers

Having a variety of information about user behavior allows financial companies to find out what customers want at the moment, and moreover what they are willing and able to pay for. So, for example, if a client was looking at ads from car dealers, then it might make sense to develop a personalized loan offer — of course, after analyzing his solvency and all possible risks.

Customer Retention

Modern AI systems working with big data in banking can not only analyze, but also can make assumptions. For example, in a number of cases, it is possible to predict the intentions of the client if he wants to refuse the services of a banking organization. The knowledge of this intention signals that it is necessary to take additional retention measures, create even more targeted and personalized offers, and as a result, improve the customer experience.

Machine Learning for Safe Bank Transactions

The main advantage of machine learning for the financial sector in the context of fraud prevention is that systems are constantly learning. In other words, the same fraudulent idea will not work twice. This works great for credit card fraud detection in the banking industry.

How Artificial Intelligence Makes Banking Safe

Most financial transactions are made when the user pays for purchases on the Internet or at brick-and-mortar businesses. This means that most fraudulent transactions also occur under the pretext of buying something. AI in banking provides an opportunity to prevent this from happening. For example:

  • Cameras with face recognition can determine whether a credit card is in the hands of the rightful owner when buying at a physical point of sale.
  • Tracking suspicious IP addresses from which a financial transaction occurs may help prevent fraud with discount coupons as well as identify fraudulent intentions. For example, if someone buys a product in order to return a fake one in its place.

Market Research and Prediction

Machine learning in conjunction with big data can not only collect information but also find specific patterns. For example, it is possible to foresee currency fluctuations, determine the most profitable ideas for investing, level credit risks (and also find a middle ground between the lowest risks and the most suitable loan for a specific user), study competitors, and identify security weaknesses.
 

Cost Reduction

Machine Learning allows financial organizations to identify weaknesses in processes and organize the work of full-time employees more efficiently. The simplest example is chatbots, which can successfully cope with advising clients on simple and standard issues. Chatbots also don’t require payment for their work! Besides the fact that working with ML allows companies to reduce costs, it is logical that it also helps increase profits due to improved customer service.

Machine Learning Use-Cases in American Banks

Here are some examples of how machine learning works at leading American banks.

JP Morgan Chase

This leading bank in the United States has developed a smart contract system called Contract Intelligence (COiN). The algorithm based on data and machine learning helps quickly find necessary documents and important information contained in them. At the moment, the bank works with more than 12,000 loan contracts and it would take several years to analyze them manually. Now Chase is working to find ways to further apply this data – for example, to train the system to search for patterns and make assumptions based on them.

Bank of America

The chatbot from this bank is a real financial consultant and strategist. The system analyzes user data and warns in cases where the client has showed slightly different buying habits and reminds him of the need to pay his bills. Bank of America’s chatbot also knows how to perform simple operations with bank cards, such as blocking and unblocking cards.

Wells Fargo

This bank has developed a smart chatbot to turn interaction with the site into a simple and convenient procedure. Wells Fargo bank developed the Predictive Banking analytics system, which is able to notify customers about unusual situations; for example, if the client has spent more than the average amount of his checks. The system may also offer to save a certain amount on a deposit if the client received a money transfer that is larger than the amount of money he usually keeps in his account.

Citibank

Citibank has developed a powerful fraud prevention system that tracks abnormalities in user behavior. In particular, the system is polished to detect fraudulent credit card transactions when shopping on the Internet.

US Bank

This bank has developed the Expense Wizard, an application that allows clients to manage their accounts as well as book airline tickets and accommodations abroad. This app focuses on secure payments in other countries. It is very convenient for those who go on a business trip without a corporate credit card, since the application allows the user to collect all financial data about the trip in one place and create a report for his company’s financial department.

Are There Any Risks in Adopting Machine Learning for Banking?

Of course, Artificial Intelligence technology can revolutionize the banking sector. However, there are certain risks — but they are mostly associated with the novelty of technologies and the lack of full understanding among users about how they really work.

Job Cuts

This is one of the most common risks and fears associated with AI and machine learning, even regardless of their scope of application. However, modern research suggests that Artificial Intelligence in the banking sector will provide a much larger number of new jobs compared to the number of professions that will become unclaimed. Also, do you remember the study we talked about at the beginning of this article? Sixty percent of AI talents are hired by financial institutions. This already gives sufficient reason to say we should not expect a total collapse.

Less Trust Due to Less Human Contact

There is also an opinion that users will feel less confidence in financial institutions because of fewer opportunities to work with human consultants. This is true, but only partially. Most likely we will observe this trend, but only in relation to people born in the previous generation, who are not too inclined to believe in technology to begin with. But as for the generation of millennials who are willing to pay more for convenience and reliability, they will be glad for the opportunity to perform any operation in a few clicks.

Ethical Risks

Ethical risks are associated with the fact that the amount of data financial companies collect, store, systematize, analyze, and use to their advantage (as well as to the benefit of customers) continues to grow. Some users do not like this trend, but at the moment it is impossible to take any action without leaving a trace of personal data. Fraudsters most of all do not like this fact, since they are already beginning to feel it is becoming harder and harder to trick AI systems. At the same time, this is a definite plus for improving the user experience and enhancing the level of security.
 

False-Positive Results Risks

Machine learning systems and AI track patterns of user behavior and compare them with accepted versions of the norm in relation to each user. So, for example, if a user completes a transaction abroad, but he has not notified the bank about his trip (or the bank for some reason could not catch this information; for example, the user did not buy the ticket from his credit card, but received it as a gift), then this operation can be interpreted as fraudulent. But in fact, everything was legal – just a small lack of information led to a false-positive result.
 

How to Choose the Best Partner to Develop Machine Learning Solutions for Your Financial Service

By introducing AI into their business processes, financial organizations should clearly understand their goals — because simply analyzing data is not the ultimate goal; AI is a way to help achieve a specific goal. Therefore, when developing an AI and ML solution for a bank or another financial company, you need to make sure that the company you entrust this task with understands the specifics of your business and is aware of what tasks this software should complete.

 

In addition, when choosing a potential AI vendor, make sure the company already has experience in developing solutions specifically for the financial sector. Why? Because the security requirements are higher than in any other field, perhaps only with the exception of healthcare. Here is our article on Top of 6 AI Companies with more detailed advice on choosing the right vendor.
 

Conclusion

Artificial Intelligence and Machine Learning in the financial sector can make these organizations more profitable and increase client trust. However, for this to happen, your AI solution must be developed by a competent team of specialists. SPD Group already has experience in developing Machine Learning and Artificial Intelligence for financial institutions. 

Originally posted here

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