Machine Learning in Banking

Artificial Intelligence and Machine Learning are changing the world right now. The banking industry is not an exception. In this article, I would like to talk about the way that technology changes this giant industry.

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 by 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.

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