An Ensemble Model for Predicting Retail Banking Churn in the Youth Segment of Customers
Data
; 7(5):61, 2022.
Article
in English
| ProQuest Central | ID: covidwho-1871909
ABSTRACT
(1) This study aims to predict the youth customers’ defection in retail banking. The sample comprised 602 young adult bank customers. (2) The study applied Machine learning techniques, including ensembles, to predict the possibility of churn. (3) The absence of mobile banking, zero-interest personal loans, access to ATMs, and customer care and support were critical driving factors to churn. The ExtraTreeClassifier model resulted in an accuracy rate of 92%, and an AUC of 91.88% validated the findings. (4) Customer retention is one of the critical success factors for organizations so as to enhance the business value. It is imperative for banks to predict the drivers of churn among their young adult customers so as to create and deliver proactive enable quality services.
Sciences: Comprehensive Works; retail banking; customer churn; machine learning; young adults; ensemble model; digital; Customer services; Banking industry; Generation Z; Data mining; Customer relationship management; Online banking; Data science; Baby boomers; COVID-19; Brand loyalty; Marketing; Customer satisfaction; Bank accounts; Pandemics; Quality of service; Age groups; Millennials; Loans; Literature reviews; Algorithms; Financial services; Customer retention
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Collection:
Databases of international organizations
Database:
ProQuest Central
Type of study:
Prognostic study
Language:
English
Journal:
Data
Year:
2022
Document Type:
Article
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