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  • Gradient boosting vs. deep learning. Possibilities of using artificial intelligence in banking
    Retail banking is undergoing radical change. New players in the market and changes in the regulatory framework (e.g. PSD 2) mean that the market is being opened up as a result of market conditions and regulations, posing new challenges for traditional banks. Customer behaviour has also changed decisively. The willingness of customers to change accounts has doubled in the last four years, with the absolute number of accounts in the German market remaining constant over this period. The traditional instruments of financial institutions currently offer hardly any opportunities to react to the effects of this change.
  • Artificial intelligence in banking: Deep learning vs. gradient boosting
    "Artificial intelligence" is one of the most discussed topics in public today. Events such as the evaluation of voter data by Cambridge Analytica for the US presiden-tial election in 2016 or the generation of a conversation with the new generation of Google Assistants, which many participants regard as humane, do fuel fears and en-thusiasm for the new technology.
  • Security in agile procedures
    Software and infrastructure projects are subject to fundamental change. The trend today is towards small, agile teams that work independently of each other and often spatially separated from each other. They develop completed subprograms that communicate with each other via coordinated interfaces and thus form an overall system. The agile approach brings with it fundamental advantages that are particularly useful in complex projects.