Abstract
Collecting various types of data about users/clients in order to improve the services and competitiveness of companies has a long history. However, these approaches are often based on classical statistical methods and an assumption of limited computational power. In this paper we introduce the vision of our applied research project targeting to the financial sector. Our main goal is to develop an automated software solution for similarity modeling over big and semi-structured graph data representing behavior of bank clients. The main aim of similarity models is to improve the decision process in risk management, marketing, security and related areas.
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Acknowledgments
This work was supported in part by the Technology Agency of the Czech Republic (TAČR) project no. TH03010276 and by Czech Science Foundation (GAČR) project no. 17-22224S.
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Skopal, T., Peška, L., Holubová, I., Paščenko, P., Hučín, J. (2018). Advanced Analytics of Large Connected Data Based on Similarity Modeling. In: Marchand-Maillet, S., Silva, Y., Chávez, E. (eds) Similarity Search and Applications. SISAP 2018. Lecture Notes in Computer Science(), vol 11223. Springer, Cham. https://doi.org/10.1007/978-3-030-02224-2_16
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