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Title: Customer classification and decision making in the digital economy based on scoring models
Authors: Lavrov, Ruslan
Burkina, Natalia
Popovskyi, Yurii
Vitvitskyi, Serhii
Korniichuk, Oleksandr
Kozlovskyi, Serhii
Keywords: modelling, decision making, algorithms, scoring models, customer classification, digital economy, cluster analysis
Issue Date: 2020
Abstract: The article presents how cluster models works to create customer classification and to make managerial de-cision for saving clients and founding new target auditory. The objective of research is to find out the relevant techniques for building scoring models in different fields. The main hypothesis of research was checking the quantity of scoring models in different fields. It was applied k-nearest neighbors support vector method for decision making in the digital economy based on scoring models. In order to realize the principle of customer classification and revealing the client categories with risk of leaving the company it was created the client’s classification model. Moreover, risk issue was shown on the example of fraud dynamic. It was researched different categories of fraud and pointed out their features. According the results of the building models it was proposed some recommendation about decision making in the risk situation. The model shows how to save existing clients and how to share client base through the finding of client groups portraits and how to be carefully in the risk situation.
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