BUY NOW PAY LATER SERVICES ON GENERATION Z: EXPLORATORY DATA ANALYSIS USING MACHINE LEARNING

YOSY ARISANDY, YOSY ARISANDY and YOSZA BIN DASRIL, YOSZA BIN DASRIL and SHAHRUL NIZAM BIN SALAHUDIN, SHAHRUL NIZAM BIN SALAHUDIN and MUCH AZIZ MUSLIM, MUCH AZIZ MUSLIM and ARISMAN ADNAN, ARISMAN ADNAN and GOH KHANG WEN, GOH KHANG WEN (2023) BUY NOW PAY LATER SERVICES ON GENERATION Z: EXPLORATORY DATA ANALYSIS USING MACHINE LEARNING. Journal of Theoretical and Applied Information Technology, 101 (11). pp. 4194-4204. ISSN 1992-8645

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Abstract

The buy now, pay later (BNPL) business model is an innovative approach to installment loans. It allows customers to take immediate possession of their purchase, with or without a down payment. Furthermore, the majority of BNPL loans are set up to require four payments. However, this type of loan comes with its own set of risks and challenges. This article examines the risk of BNPL as a product for consumers known as Generation Z. The data used is secondary data provided by Kaggle in csv format (loan data.csv) contains 159,584 postpaid customer records and 28 features analyzed through descriptive and Exploratory Data Analysis (EDA). The results show that the majority of pay later clients are married and known as millennials are the ones who used pay later services the most (52.10%). Generation Z has the greatest rate of loan defaults which is about 34.16% with the time employee is about 0-8 months (35.8%). Furthermore, the results indicated that the unemployed generation Z has the highest default percentage of 32.16%. This Exploration data analytic is viewed as a step towards gaining a better understanding of consumers so that predictions, suggestions, and recommendations can be made for potential customers and market paylater segmentation to find the right target market, thereby positively impacting company profits.

Item Type:	Article
Uncontrolled Keywords:	Buy Now Pay Later, Risky, Generation Z, Exploratory Data Analysis, Machine Learning
Subjects:	T Technology > T Technology (General)
Divisions:	Faculty of Technology Management and Business > Department of Business Management
Depositing User:	Mr. Mohamad Zulkhibri Rahmad
Date Deposited:	03 Jan 2024 01:35
Last Modified:	03 Jan 2024 01:35
URI:	http://eprints.uthm.edu.my/id/eprint/10532

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