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My perspective on Machine Learning in Banking

Banking is one of those industries that are hard to change. However, with the rising popularity of Artificial Intelligence, Machine Learning and Chatbots things are looking to change. I would like to share my thoughts on the ways that technology will change the industry forever, and what we should expect.

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.

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 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.

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