Modelling bank customer behaviour using feature engineering and classification techniques

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F23%3A39920844" target="_blank" >RIV/00216275:25410/23:39920844 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0275531923000399" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0275531923000399</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.ribaf.2023.101913" target="_blank" >10.1016/j.ribaf.2023.101913</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Modelling bank customer behaviour using feature engineering and classification techniques

Popis výsledku v původním jazyce

This study investigates customer behaviour and activity in the banking sector and uses various feature transformation techniques to convert the behavioural data into different data structures. Feature selection is then performed to generate feature subsets from the transformed datasets. Several classification methods used in the literature are applied to the original and transformed feature subsets. The proposed combined knowledge mining model enable us to conduct a benchmark study on the prediction of bank customer behaviour. A real bank customer dataset, drawn from 24,000 active and inactive customers, is used for an experimental analysis, which sheds new light on the role of feature engineering in bank customer classification. This paper&apos;s detailed systematic analysis of the modelling of bank customer behaviour can help banking institutions take the right steps to increase their customers&apos; activity.

Název v anglickém jazyce

Modelling bank customer behaviour using feature engineering and classification techniques

Popis výsledku anglicky

This study investigates customer behaviour and activity in the banking sector and uses various feature transformation techniques to convert the behavioural data into different data structures. Feature selection is then performed to generate feature subsets from the transformed datasets. Several classification methods used in the literature are applied to the original and transformed feature subsets. The proposed combined knowledge mining model enable us to conduct a benchmark study on the prediction of bank customer behaviour. A real bank customer dataset, drawn from 24,000 active and inactive customers, is used for an experimental analysis, which sheds new light on the role of feature engineering in bank customer classification. This paper&apos;s detailed systematic analysis of the modelling of bank customer behaviour can help banking institutions take the right steps to increase their customers&apos; activity.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50206 - Finance

Projekt

<a href="/cs/project/GA19-15498S" target="_blank" >GA19-15498S: Modelování emocí ve verbální a neverbální manažerské komunikaci pro predikci podnikových finančních rizik</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Research in International Business and Finance

ISSN

0275-5319

e-ISSN

1878-3384

Svazek periodika

65

Číslo periodika v rámci svazku

April

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

16

Strana od-do

101913

Kód UT WoS článku

000951433200001

EID výsledku v databázi Scopus

2-s2.0-85149284943