Optimization of the BANK's Branch Network Using Machine Learning Methods

被引：0

作者：

Ardan, Dorzhiev ^{[1
]}

机构：

[1] Financial Univ Govt Russian Federat, 49 Leningradsky Prospekt, Moscow 125993, Russia

来源：

INTELLIGENT SYSTEMS AND APPLICATIONS, VOL 1 | 2023年 / 542卷

关键词：

Geoanalysis; Geomarketing; Branch network; Banks; Machine learning; ANALYTICS; OPENSTREETMAP; SYSTEM;

D O I：

10.1007/978-3-031-16072-1_38

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The pandemic has made many companies move their business processes to online. Financial institutions, especially banks, have been quite active in developing online capabilities of their front offices. Having an extensive branch structure, they were faced with the need to re-assess the current efficiency of their entire networks and take a closer look at their individual front offices. Both new planned branches and the currently active ones require reassessment if they can be competitive with the online channel. New technologies provide new tools to examine the network and make decisions on its optimization. This article offers an operational assessment example of a financial company's front office network in the city of Moscow. The proposed machine learning model for predicting customer traffic enables to determine the most relevant locations for office rent. As an open source of realty data a leading real estate aggregator cian.ru has been chosen.

引用

页码：514 / 530

页数：17

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