Commerzbank work with Retresco to create chatbots


24.05.2018, Berlin: Commerzbank is looking to improve its internal knowledge management with the introduction of an internal enterprise platform for building and deploying chatbots. The project will be undertaken in cooperation with Retresco.

The project began with Commerzbank launching three chatbots at the beginning of the year. One was for employees who needed assistance for tasks such as whom to contact and what to do if they lose items such as IDs or keys. Two further chatbots were developed to cover areas including purchasing, tendering processes, and authorisations. The chatbots optimize the bank’s internal processes so frequently asked questions are answered comprehensively, quickly and completely. Instead of having to trawl through internal systems in order to find answers, employees can now ask a chatbot. By means of direct feedback about the entered user requests, the chatbots are continuously optimized to better adapt the answers to the requirements of the employees.

Ulrich Pöttgens, Commerzbank AG:
“With the introduction of chatbots, our employees benefit from significantly faster and solution-oriented support. After a successful start, we now plan to develop chatbots that will answer frequently used questions.  This will arrange our processes so that they will become more user-oriented and efficient.”

Commerzbank’s chatbots are based on a platform specially developed by Retresco that allows chatbots to be utilised across all of the bank’s internal activities. Following Retresco’s initial development of the platform, the bank is now able to develop, train and optimize its own chatbots. The companies want to have achieved this goal by the summer of 2018.

Alexander Siebert, founder and CEO of Retresco, said,
“After the introduction of the tool, every specialist department at Commerzbank can create their own bots. This makes knowledge management much easier as it is often unclear what questions and needs the employees have. With this in mind, we have developed a system that learns from and reacts to specific feedback from staff.”

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