Exploration of the investment patterns of potential retail banking customers using two-stage cluster analysis

Abstract Identifying investment patterns as part of customer segmentation is one of the most important tasks in retail banking. Clustering customers effectively is an important element of improving marketing policy and strategic planning. There are several methods for identifying similar groups of c...

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Autores principales:	Tibor Kovács, Andrea Ko, Asefeh Asemi
Formato:	article
Lenguaje:	EN
Publicado:	SpringerOpen 2021
Materias:	Investment Patterns Factors Affecting Investment Customer Clustering Retail Banking Business Kohonen Self-Organizing Maps Multiple Correspondence Analysis Computer engineering. Computer hardware TK7885-7895 Information technology T58.5-58.64 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/8eb190bd7d7d45f2ba29709db785ab53
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spelling	oai:doaj.org-article:8eb190bd7d7d45f2ba29709db785ab532021-11-07T12:02:39ZExploration of the investment patterns of potential retail banking customers using two-stage cluster analysis10.1186/s40537-021-00529-42196-1115https://doaj.org/article/8eb190bd7d7d45f2ba29709db785ab532021-11-01T00:00:00Zhttps://doi.org/10.1186/s40537-021-00529-4https://doaj.org/toc/2196-1115Abstract Identifying investment patterns as part of customer segmentation is one of the most important tasks in retail banking. Clustering customers effectively is an important element of improving marketing policy and strategic planning. There are several methods for identifying similar groups of customers and describing their characteristics to offer them appropriate products. However, using machine learning methods is rare, and the application is limited for certain types of data. The aim of this study is to investigate the benefits of using a two-stage clustering method using neural-network-based Kohonen self-organizing maps followed by hierarchical clustering for identifying the investment patterns of potential retail banking customers. The unique benefit of this method is the ability to use both categorical and numerical variables at the same time. This research examined 1,542 responses received for an online investment survey, focusing on the questions that are related to the respondents’ investment preferences and their current financial assets. The research utilizes descriptive statistics and multiple correspondence analysis (MCA) to understand the variables and Kohonen self-organizing maps (SOMs), in combination with hierarchical clustering, to identify customer groups and describe the characteristics of these clusters. The analysis was able to identify clusters of potential customers with similar preferences and gained insights into their investment patterns related to their investment portfolio and investment behavior, including their savings profile, attitude to risk-taking, and preferences for investment advice. These findings were supported by additional insights through the application of multiple correspondence analysis (MCA) describing patterns of financial instruments and portfolios. The main contribution of the research is the combined application of the machine learning methods Kohonen SOM, hierarchical clustering, and MCA for investment pattern analysis in the retail banking business.Tibor KovácsAndrea KoAsefeh AsemiSpringerOpenarticleInvestment PatternsFactors Affecting InvestmentCustomer ClusteringRetail Banking BusinessKohonen Self-Organizing MapsMultiple Correspondence AnalysisComputer engineering. Computer hardwareTK7885-7895Information technologyT58.5-58.64Electronic computers. Computer scienceQA75.5-76.95ENJournal of Big Data, Vol 8, Iss 1, Pp 1-25 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Investment Patterns Factors Affecting Investment Customer Clustering Retail Banking Business Kohonen Self-Organizing Maps Multiple Correspondence Analysis Computer engineering. Computer hardware TK7885-7895 Information technology T58.5-58.64 Electronic computers. Computer science QA75.5-76.95
spellingShingle	Investment Patterns Factors Affecting Investment Customer Clustering Retail Banking Business Kohonen Self-Organizing Maps Multiple Correspondence Analysis Computer engineering. Computer hardware TK7885-7895 Information technology T58.5-58.64 Electronic computers. Computer science QA75.5-76.95 Tibor Kovács Andrea Ko Asefeh Asemi Exploration of the investment patterns of potential retail banking customers using two-stage cluster analysis
description	Abstract Identifying investment patterns as part of customer segmentation is one of the most important tasks in retail banking. Clustering customers effectively is an important element of improving marketing policy and strategic planning. There are several methods for identifying similar groups of customers and describing their characteristics to offer them appropriate products. However, using machine learning methods is rare, and the application is limited for certain types of data. The aim of this study is to investigate the benefits of using a two-stage clustering method using neural-network-based Kohonen self-organizing maps followed by hierarchical clustering for identifying the investment patterns of potential retail banking customers. The unique benefit of this method is the ability to use both categorical and numerical variables at the same time. This research examined 1,542 responses received for an online investment survey, focusing on the questions that are related to the respondents’ investment preferences and their current financial assets. The research utilizes descriptive statistics and multiple correspondence analysis (MCA) to understand the variables and Kohonen self-organizing maps (SOMs), in combination with hierarchical clustering, to identify customer groups and describe the characteristics of these clusters. The analysis was able to identify clusters of potential customers with similar preferences and gained insights into their investment patterns related to their investment portfolio and investment behavior, including their savings profile, attitude to risk-taking, and preferences for investment advice. These findings were supported by additional insights through the application of multiple correspondence analysis (MCA) describing patterns of financial instruments and portfolios. The main contribution of the research is the combined application of the machine learning methods Kohonen SOM, hierarchical clustering, and MCA for investment pattern analysis in the retail banking business.
format	article
author	Tibor Kovács Andrea Ko Asefeh Asemi
author_facet	Tibor Kovács Andrea Ko Asefeh Asemi
author_sort	Tibor Kovács
title	Exploration of the investment patterns of potential retail banking customers using two-stage cluster analysis
title_short	Exploration of the investment patterns of potential retail banking customers using two-stage cluster analysis
title_full	Exploration of the investment patterns of potential retail banking customers using two-stage cluster analysis
title_fullStr	Exploration of the investment patterns of potential retail banking customers using two-stage cluster analysis
title_full_unstemmed	Exploration of the investment patterns of potential retail banking customers using two-stage cluster analysis
title_sort	exploration of the investment patterns of potential retail banking customers using two-stage cluster analysis
publisher	SpringerOpen
publishDate	2021
url	https://doaj.org/article/8eb190bd7d7d45f2ba29709db785ab53
work_keys_str_mv	AT tiborkovacs explorationoftheinvestmentpatternsofpotentialretailbankingcustomersusingtwostageclusteranalysis AT andreako explorationoftheinvestmentpatternsofpotentialretailbankingcustomersusingtwostageclusteranalysis AT asefehasemi explorationoftheinvestmentpatternsofpotentialretailbankingcustomersusingtwostageclusteranalysis
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Exploration of the investment patterns of potential retail banking customers using two-stage cluster analysis

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