The new take on customer data

Phani Kumar S on January 24, 2019

Today, there are a number of new ways in which we can communicate. For this, all credit goes to the technology companies. Atop that, we also have new and better ways to purchase goods that we want and to sell the ones that we do not need.

There is no doubt that we can rely on the technology companies a big time when it comes to the hyper-personalization of customer experiences and the creation of better algorithms to drive new innovations. However, whether or not we can rely on these companies with regard to the privacy of customer data is a question that lies in the grey area. Will the technology companies drive all the innovation without breakage of the trust bond?

Over the last couple of years, technology has been finding its way into every business sector. This is primarily because every industry is being reshaped by technology. In this aspect, the banking sector is not different either.

Currently, it looks like the banking industry is grappling with developing a backbone for banking driven by customer data and technology while maintaining the legacy of trust. This is not a simple task at all.

Previously, the data was nothing other than the byproduct of the customer relationship. In other words, data was collected and protected. However, today, new technologies such as artificial intelligence have powered the banks up. As a result of this, banks now carry an opportunity to modify their approach to customer data based on modern needs. For instance, TD in Canada focuses on the use of customer data for the development of a highly personalized experience.

An artificial intelligence company that which TD Bank acquired earlier in 2018 happened to be the winner of RecSys challenge. This challenge is a highly prestigious competition that occurs in the Artificial Intelligence community on a global scale. This happened for the second time in a row. In the 2018 RecSys challenge, the artificial intelligence teams managed to personalize the playlists of users and to add songs into the user’s playlist based on the user’s specific taste in music. This was made possible by developing an algorithm which would analyze the Spotify Million Playlist Dataset which was itself composed of more than 1000000 playlists generated by users.

This very logic is now also being applied to banking, in order to allow people to reach their financial goals and to be where they aspire to be. There are a number of different applications to this. For instance, a customer can be notified that based on their latest patterns of spending, they may face a financial crisis in the coming time. Artificial Intelligence technology can further be used to help them out in making the necessary changes in their spending pattern.

Similarly, the use of artificial intelligence systems can also help banks like TD in creating a rather personalized strategy for investment that will allow customers to achieve their retirement aspirations and goals. With regard to this, a number of measured approaches are being taken regarding the way data need to be put into work.

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