Natural Language Generation in the Financial Sector – Practical Experience

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It is always exciting to see in how many sectors NLG is used successfully. Today Mario Berger, Director at Deloitte, reports on his experiences with the AX Semantics Natural Language Generation software in Financial Services.

AX: Hello Mario, please introduce yourself to our readers.

MB: I have been in the financial services industry for more than 20 years and have been responsible for the introduction of Natural Language Generation at Deloitte. I started my career at a major German credit institution before moving to banking and insurance consulting. My focus is on reporting and regulatory reporting, the implementation of regulatory requirements for reporting, and the digitization of all these topics. I also moderate various formats such as strategy, innovation, and business workshops and manage projects of varying scope and content.

When did you start to focus on Natural Language Generation (NLG)?

It all started with my first projects because process efficiency and optimization were already at the top of the agenda in regulatory reporting. For example, when implementing regulatory requirements, we have always considered how to set up repetitive or pattern-based parts of reporting using rule-based systems and curated templates. Since early 2018, the Deloitte Network for Language Technology has been pushing further development of “language technology.” Together with research institutions, universities, and solution providers, we are working to ensure that technology transfer into today’s process world is successful. And we also are looking at ways to increase the acceptance of NLG in the public sector and in companies. 

What potential does Deloitte see in automated text generation?

If one refers to the creation of certain recurring reports, various projects have already shown that the effort can be reduced by up to 80%. In our estimation, projects in this environment are significantly more successful if parts or even the entire “path” of the process is automated – i.e. data management, for example, using Natural Language Processing. We consider the fundamental potential to be very high. 

We are also currently working on a study that deals with experience in NLG handling and the findings regarding cost and process efficiency.

What concrete projects has Deloitte realized with NLG?

Our focus is on the reporting of established processes. In addition to the successful implementation of risk and compliance reports, we examine whether NLG can also be used efficiently in the area of finance or balance sheet preparation. There is also considerable potential in the area of auditing, and with DNAV we already offer a digital solution for Smart Fund Audit Analytics. 

 

Why did you choose to wirk with AX Semantics?

The Text Engine of AX Semantics fulfills all essential aspects for the generation of the mentioned reports from our point of view. Quality and applicability of the solution are very high, which was also confirmed by the Forrester report from October 2018. And last but not least, the operational cooperation between the teams is excellent, and we get on really well with the AX Management Team. 

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Nice Use Case for our software! With AX Semantics, these things can be excellently generated automatically and thus save a lot of resources. To automatically generate texts, our tool needs data as an information base.

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With AX Semantics, data-based written content can be excellently generated automatically and thus save a lot of resources.

However, you only need a relatively small amount of text. Our tool, instead, is more worthwhile the larger the amount of text you need – or to realize completely new content formats which cannot be implemented manually due to time constraints.

If you are curious, feel free to take the free tour through the tool and see if it might be worth it for you.

With AX Semantics, data-based written content can be excellently generated automatically and thus save a lot of resources.

Our tool is more worthwhile the larger the amount of text you need – or to realize completely new content formats which cannot be implemented manually due to time constraints. Have you ever thought about category texts? Or personalized newsletters?

Take the free tour through the tool and see if it might be worth it for you.

AX Semantics is normally used to bring your data into written content. Signup for the free tour and see how that works with a real example - and then let's see together what we could do for you?

You have the best prerequisites to make repetitive writing faster and easier with AX Semantics. Number-based reports, studies or forecasts can be automated excellently with AX Semantics and thus save a lot of resources.

Sign up and start the free tour through the tool!

Using AX Semantics can save you a lot of resources. To automatically generate texts, our tool needs data as an information base.

Let's have a conversation to find out what information could serve as a basis for your texts.

Just fill out the form and one of our content experts will contact you!

With AX Semantics, category or product content can be excellently generated automatically and thus save a lot of resources.

However, you only need a relatively small amount of text. Our tool, instead, is more worthwhile the larger the amount of text you need - or to realize completely new content formats which cannot be implemented manually due to time constraints. Have you ever thought about category texts? Or personalized newsletters?

If you are curious, feel free to take the free tour through the tool and see if it might be worth it for you.

With AX Semantics, listing content can be excellently generated automatically and thus save a lot of resources.

However, you only need a relatively small amount of text. But this can be used in real time applications, driving your users to signup faster and better!

If you are curious, feel free to take the free tour through the tool and see if it might be worth it for you.

With AX Semantics, category or product content can be excellently generated automatically and thus save a lot of resources.

Our tool is more worthwhile the larger the amount of text you need – or to realize completely new content formats which cannot be implemented manually due to time constraints. Have you ever thought about category texts? Or personalized newsletters?

Take the free tour through the tool and see if it might be worth it for you.

With AX Semantics, listing content can be excellently generated automatically and thus save a lot of resources.

Our tool is more worthwhile the larger the amount of text you need – or to realize completely new content formats which cannot be implemented manually due to time constraints. Have you ever thought about real time applications, driving your users to convert faster and better?

Take the free tour through the tool and see if it might be worth it for you.

With AX Semantics, written content can be excellently generated automatically and thus save a lot of resources.

Our tool is more worthwhile the larger the amount of text you need – or to realize completely new content formats which cannot be implemented manually due to time constraints. Have you ever thought about real time applications, driving your users to convert faster and better?

Take the free tour through the tool and see if it might be worth it for you.

With AX Semantics you can bring your data into written content. Signup for the free tour, and see how that works with a real example.

AX Semantics is normally used to bring your data into written content. Signup for the free tour, and see how that works with a real example. - and then let's see together what we could do for you?

AX Semantics is especially worthwhile to use it if you create a lot of repetitive content. In terms of content, the texts you create always say something different, but the "events" repeat themselves. And that is also the point at which the tool scales.

Blog posts are usually not repetitive and not based on structured data. Therefore the implementation of blog posts is not the optimal use case.

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