Bova R.Yu. The use of graph neural networks to build a digital double of a client in the ̳eld of housing mortgage lending

Authors

  • admin admin

Keywords:

Data mining, graph neural networks (GNN), digital twin, residential mortgage lending, consumer behavior modeling

Abstract

Housing mortgage lending is an important tool of the modern economy. It performs an important social function, supporting the population of the country when purchasing new residential areas. At the moment, housing mortgage lending is under active development. Taking into account external and internal factors, the economy, including the sphere of mortgage fluctuations, is undergoing significant changes. This creates increased scientific interest for the development of existing approaches to modeling consumer behavior and for the creation of new approaches and tools. With the development of modern methods of information collection and universal global digitalization, opportunities for creating decision-making systems based on digital models have increasingly begun to open up. One of the most popular and important research areas for residential mortgage lending is the creation of digital client twins using modern data analysis and machine learning technologies. The article considers a way to organize a digital double of a client for residential mortgage lending. A method for the realization of such a double using graph neural network technologies is considered.

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Published

2024-09-09

How to Cite

admin, admin. (2024). Bova R.Yu. The use of graph neural networks to build a digital double of a client in the ̳eld of housing mortgage lending. DISCUSSION | Journal of Scientific Publications on Economic ISSN 2077-7639, 126(5), 29–36. Retrieved from https://discussionj.ru/index.php/polemik/article/view/245

Issue

Section

Mathematical, statistical and instrumental methods in economics(economic scienc)

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