Trust in AI depends largely on how well we understand it.

In the financial industry, data-driven automated decisions promise significant optimization and savings.

However, blind faith in AI systems is not an option due to regulatory requirements and business risks.


Sound data

We facilitate the cost-efficient creation of high-quality data sets


Reliable decisions

We enable domain experts to validate results and control AI systems


Human agency

Tailored human-AI interfaces foster users' acceptance and trust


Audit-proof data storage is the basic building block for important business decisions. It is therefore important to ensure the consistency and completeness of data over the entire retention period. This means that the results of the specialist process can be traced retrospectively on the basis of the stored data.

Unreliable data leads to the actual context of the data being lost or no longer being correct. It is therefore essential to reliably adhere to compliance regulations and to document access to data in a traceable manner so that data cannot be changed or manipulated without authorization.

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The goal of the tailored use of Explainable AI is to promote transparency and comprehensibility and thus to make better decisions. Only if users understand the background of a decision or recommendation are they able to critically examine it.

Explanations of decisions made by AI systems must be target group-specific and understandable in order to create real added value. We use different algorithms to generate explanations with different levels of detail and purpose.

In this way, we ensure that AI is used in your company in a trustworthy and traceable manner - and without any intervention in the functioning of the AI system.

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Find out how we can help you take your AI projects to the next level. Book a demo with one of our experts!

Especially in the financial industry, the acceptance of the systems is of fundamental importance, as the applications involve critical IT infrastructures and sensitive data.

We design user-centric explanations by making the system outputs of AI applications comprehensible.

Transparency and the ability to articulate why a particular decision is made by the AI system is critical to trust and thus adoption of machine learning applications.

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Explain. Justify. Build trust. Learn more about designing interactive user experiences with Explainable AI!