Clustering Based on Customers’ Behaviour in Accepting Personal Loan using Unsupervised Machine Learning
This research explores the application of unsupervised learning, a subset of Artificial Intelligence (AI), to analyze customer behavior in accepting personal loans within the banking sector. Focusing on clustering algorithms, the study employs popular methods like K-Means Clustering, Density-Bas...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
INTI International University
2023
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| Subjects: | |
| Online Access: | http://eprints.intimal.edu.my/1885/1/ij2023_72.pdf http://eprints.intimal.edu.my/1885/ https://intijournal.intimal.edu.my |
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| Summary: | This research explores the application of unsupervised learning, a subset of Artificial Intelligence
(AI), to analyze customer behavior in accepting personal loans within the banking sector. Focusing
on clustering algorithms, the study employs popular methods like K-Means Clustering, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Agglomerative Hierarchical
Clustering, and Mean Shift Clustering to understand customer characteristics and behaviors. Using
a dataset from Kaggle comprising 13 attributes and 5000 rows of bank customer data, the research
addresses the challenge of processing overwhelming customer information by leveraging machine
learning models. The objective is to enhance target marketing campaigns, increase success ratios,
and identify potential customers with a higher probability of loan acceptance. This research
contributes novel insights into the application of clustering algorithms in banking, proposing
pragmatic solutions for efficient data analysis and campaign optimization. The findings underscore
the pivotal role of AI in navigating and unraveling customer behavior complexities in the banking
industry. All clustering models are developed successfully in producing cluster results. Besides,
this system was able to perform good data visualization in order to provide better user experience.
All the models are compared and discussed based on the results obtained. As a conclusion, KMeans clustering was presenting better cluster results using this particular dataset. |
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