BUY NOW PAY LATER SERVICES ON GENERATION Z: EXPLORATORY DATA ANALYSIS USING MACHINE LEARNING
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 c...
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| Format: | Article |
| Language: | English |
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jatit
2023
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| Online Access: | http://eprints.uthm.edu.my/10108/1/J16236_f65f1e2f56b7f4788620755a34385654.pdf |
| _version_ | 1796870102368387072 |
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| author | ARISANDY, YOSY DASRIL, YOSZA SALAHUDIN, SHAHRUL NIZAM MUSLIM, MUCH AZIZ ADNAN, ARISMAN GOH KHANG WEN, GOH KHANG WEN |
| author_facet | ARISANDY, YOSY DASRIL, YOSZA SALAHUDIN, SHAHRUL NIZAM MUSLIM, MUCH AZIZ ADNAN, ARISMAN GOH KHANG WEN, GOH KHANG WEN |
| author_sort | ARISANDY, YOSY |
| collection | UTHM |
| description | 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. |
| first_indexed | 2024-03-05T22:04:34Z |
| format | Article |
| id | uthm.eprints-10108 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | English |
| last_indexed | 2024-03-05T22:04:34Z |
| publishDate | 2023 |
| publisher | jatit |
| record_format | dspace |
| spelling | uthm.eprints-101082023-10-17T06:56:26Z http://eprints.uthm.edu.my/10108/ BUY NOW PAY LATER SERVICES ON GENERATION Z: EXPLORATORY DATA ANALYSIS USING MACHINE LEARNING ARISANDY, YOSY DASRIL, YOSZA SALAHUDIN, SHAHRUL NIZAM MUSLIM, MUCH AZIZ ADNAN, ARISMAN GOH KHANG WEN, GOH KHANG WEN HG3691-3769 Credit. Debt. Loans Including credit institutions, credit instruments, consumer credit, bank- ruptcy 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. jatit 2023 Article PeerReviewed text en http://eprints.uthm.edu.my/10108/1/J16236_f65f1e2f56b7f4788620755a34385654.pdf ARISANDY, YOSY and DASRIL, YOSZA and SALAHUDIN, SHAHRUL NIZAM and MUSLIM, MUCH AZIZ and ADNAN, ARISMAN 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 |
| spellingShingle | HG3691-3769 Credit. Debt. Loans Including credit institutions, credit instruments, consumer credit, bank- ruptcy ARISANDY, YOSY DASRIL, YOSZA SALAHUDIN, SHAHRUL NIZAM MUSLIM, MUCH AZIZ ADNAN, ARISMAN GOH KHANG WEN, GOH KHANG WEN BUY NOW PAY LATER SERVICES ON GENERATION Z: EXPLORATORY DATA ANALYSIS USING MACHINE LEARNING |
| title | BUY NOW PAY LATER SERVICES ON GENERATION Z:
EXPLORATORY DATA ANALYSIS USING MACHINE LEARNING |
| title_full | BUY NOW PAY LATER SERVICES ON GENERATION Z:
EXPLORATORY DATA ANALYSIS USING MACHINE LEARNING |
| title_fullStr | BUY NOW PAY LATER SERVICES ON GENERATION Z:
EXPLORATORY DATA ANALYSIS USING MACHINE LEARNING |
| title_full_unstemmed | BUY NOW PAY LATER SERVICES ON GENERATION Z:
EXPLORATORY DATA ANALYSIS USING MACHINE LEARNING |
| title_short | BUY NOW PAY LATER SERVICES ON GENERATION Z:
EXPLORATORY DATA ANALYSIS USING MACHINE LEARNING |
| title_sort | buy now pay later services on generation z exploratory data analysis using machine learning |
| topic | HG3691-3769 Credit. Debt. Loans Including credit institutions, credit instruments, consumer credit, bank- ruptcy |
| url | http://eprints.uthm.edu.my/10108/1/J16236_f65f1e2f56b7f4788620755a34385654.pdf |
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