Public

ZAIT – Comparison to BAIT

At the same time as the Banking Authority IT Requirements (BAIT), the German Federal Financial Supervisory Authority has also updated the Payment Services Authority IT Requirements for Payment and E-Money Institutions (ZAIT). The following blog post deals with the changes to the various requirements and analyses the differences between BAIT and ZAIT. It can be said at the outset that, in comparison, six chapters have remained the same in terms of content, five chapters have changed in part and the changes in the area of "outsourcing" have changed significantly. Furthermore, ZAIT introduces more fine-grained specifications, a framework with target formulations and the freedom of implementation with appropriate measures increasingly becomes a catalogue of measures.

Value maximisation in human resources through cloud BPO

In today's company landscape, most HR departments lack an understanding and active involvement in business strategy development. Instead significant capacity is allocated for repetitive processes and commodity services such as HR management. In this blogpost, we outline strategies to transform HR from a cost-center into a primary strategy execution lever effectively eliminating administrative overhead.

Transforming the Core. Splendor and Misery of the Inevitable

Macroeconomic conditions, high regulatory pressure, and increasing customer requirements are driving impetus in operational IT management. Successful transformations require that technology management is understood as an integral part of a strategy and that strategic, organizational, and technological requirements are differentiated and taken into account in both the solution design and the control of measures. In addition, the pressure to change must be met promptly and more decisively than in the past.

Making it explicit - Comparison of prediction abilities for different AI-methods

To implement meaningful and high-quality data analyses, data preparation is of paramount importance. One step in data preparation is feature engineering - an optional process designed to make information implicit in the model explicitly accessible. Feature engineering requires the use of domain or expert knowledge to make the information explicitly available. Feature Engineering is investigated in this blog post against the background of different models using information extraction from temporal data. The consideration of domain knowledge for Feature Engineering seems to be useful and essential for the creation of more detailed analyses, also considering the effort involved.

Gradient boosting vs. deep learning. Possibilities of using artificial intelligence in banking

Artificial intelligence is growing in importance and is one of the most discussed technological topics today. The article explains and discusses two approaches and their viability for the utilization of AI in banking use cases: Deep learning and gradient boosting. While artificial intelligence and the deep learning model generate substantial media attention, gradient boosting is not as well-known to the public.
Deep learning is based on complex artificial neural networks, which process data rapidly via a layered network structure until a decision regarding a previously defined problem is made in the last layer. This enables the solution of complex problems but can lead to insufficient transparency and traceability in terms of the decision-making process, as one large decision tree is being followed. The German regulatory authority BaFin already stated that in terms of traceability no algorithms will be accepted, that is no longer comprehensible due to their complexity.

In this regard, the authors argue that gradient boosting might be a viable alternative, as this model bases its decisions on several smaller decision trees, which are easier to interpret and understand. In addition, gradient boosting requires less data than deep learning to become functional. 

Statements about the dominance of either of the models are not possible, as both of them provide good results in terms of classifying high-dimensional data. However, considering regulatory requirements, such as transparency in the financial industry, gradient boosting might be a preferable approach to artificial intelligence. 

EBA Public Hearing on Strong Customer Authentication and Secure Communication under PSD II

The EBA public hearing on Regulatory Technical Standards, specifying the requirements on strong customer authentication and common secure communication under PSD II, took place on September 23. The public hearing is an integral part of the consultation phase and regularly provides a summary of the initial consultation phase, as well as an insight into how the RTS are likely to shape up.

The Primacy of Technology

In the face of structural changes in the finance industry, financial institutions are under a high amount of pressure to adapt. After all, their entire business model is being called into question. To structure this change successfully, technologies must be used differently and their problem-solving potential must be harnessed more consistently by managers.