PDF Neukundengewinnung bei Banken (German Edition)

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Erprobung innovativer Investitionsgüter bei Erstkunden, Wiesbaden

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  4. The resilience of banks' international operations

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    In addition, we have included websites of international organizations such as the European Union. The operators as well as their customers are on thin ice legally and are subject to high risks, up to the total loss of money and BitCoins.

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    4. What's the alternative? The seemingly first alternative is to buy or sell BitCoins in larger quantities directly from private to private or from company to company. Among acquaintances, friends, business partners and companies who know and trust each other, this is also the usual and fastest way: The buyer delivers the BitCoins against payment of the agreed sum to the agreed BitCoin Wallet — ready. The main concrete risks are:. However, the official regulations for handling BitCoin deals are only in place in a few countries, which means that such transactions are still largely unregulated and therefore very uncertain.

      The aim here is to exploit the cooperation in the ensemble, whereby the individual models are created in such a way that they compensate for the deficits of the already trained submodels. Decision trees are usually used as single models, which are weighted and included in the overall result. The two models presented were tested with regard to their predictive quality using the misclassification rate - the proportion of incorrectly predicted data points and customers respectively - whereby a correct classification of a cancelling customer was weighted higher than that of a remaining customer.

      In order to ensure a better intuitive interpretability of the models in this article, the results are given in unweighted form. In the test runs, the Deep Neural Network came to an erroneous prognosis in The gradient boosting model with decision trees only came to the wrong result in 3.

      Besides the better prediction accuracy, the gradient boosting model convinced with shorter learning and prediction times.

      Kundenakquise und Neukundengewinnung: Tipps für die Neukundenakquise

      In the test scenario considered, this circumstance did not lead to a decisive reason for selection due to the relatively small data set, but for larger data sets this aspect is of increasing importance keyword Big Data. In view of the fact that, in the event of termination, not only the right forecast but also the right offer must be chosen, the interpretability of models represents a not negligible advantage. The strength of deep learning models, with their scalable architecture in depth and breadth, unfortunately leads in return to some limitations in terms of interpretability - despite significant progress in this area.

      Gradient boosting, on the other hand, offers good interpretability of both the individual decision trees and the weighted ensemble, which is an advantage in the banking environment.


      By using an interpretable model, it may be possible to draw conclusions about the reasons for the termination in addition to forecasting terminations. Even though we would have liked to have done this in our test with Banco Santander's data set, no such findings could have been obtained due to the anonymisation. Gradient boosting has proven to be the more promising solution for the described data set and use case than deep learning. In addition to the better analysis results, gradient boosting scores with better interpretability, which offers the user an improved possibility to understand the decision making of the algorithm and thus favours the ability to act in the event of termination.

      The resilience of banks' international operations

      A general statement on the dominance of one of the two algorithms cannot be made, since it depends on the patterns contained in the data. For example, Deep Neural Networks remain unchallenged in the field of image, speech and character recognition. In individual cases, it is now necessary to determine which information is actually contained in the existing data and thus whether, as with Banco Santander, a new type of customer service can be made possible.

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