How Creditreform saves valuable time through natural language generation – part 2

What exactly does Creditreform do with Natural Language Generation (NLG)?

The preparation of textual balance sheet comments is a very complex and time-consuming process and depends heavily on the experience of the credit analyst. We want to use NLG to optimize the process of commenting, to significantly reduce the manual effort, and to fully automate the process within the framework of portfolio follow-up disclosure. Currently we see a hybrid approach of human and machine intelligence. The machine prepares balance sheet comments that can then be finalized more efficiently by the credit analyst and, in the best case, reflected back to the machine intelligence.

Division of the NLG project into three learning phases

After our decision to use AX Semantics in October 2018, we divided our NLG project into three learning phases.

In the first phase, an experienced analyst from the banking sector prepared balance sheet comments, which we continuously transmitted to the software based on rules. This first learning phase took nine months. Now we are in the second phase, in which we automatically generate balance sheet comments and have them corrected by our analysts. The corrections are then taken back into the rules of regulations.

The first two phases are currently running parallel to each other in order to establish as broad a set of rules as possible. The industry-specific differentiation plays a major role here. The quality of the produced text items is so good that we want to start with the third phase. For this purpose, we are looking for customers who process our pre-produced texts in their processes and who also reflect our correction requests here. We have gained so much experience over the past 12 months that we can process these corrections on short notice on the system side. With each new text product and with each correction, we improve the quality of the texts.

Starting in November 2019, we will use the provided API to retrieve the texts via our own system and to deliver them directly to our customers’ decision-making systems.

It is also exciting that we have identified further use cases for ourselves, which are also being implemented. We will be happy to discuss this at the beginning of the new year.

You work with AX Semantics – what inspires you?

Working with the AX Semantics team is really exciting. I really loved the uncomplicated on-boarding with the integrated training concept. The support is also excellent. Whenever we have had difficulties or questions, there was immediate support. I am looking forward to further cooperation with AX Semantics.


Curious about more customer experiences with AX Semantics? Have a look at the interviews with Harry Olfert from Schäfer Shop, Mario Berger from Deloitte and Bernd Vermaaten from

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