Fraud Detection in Credit Card Transactions Using HDBSCAN, UMAP and SMOTE Methods Article Swipe
Rudy Setiawan
,
Budi Tjahjono
,
Gerry Firmansyah
,
Habibullah Akbar
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.46729/ijstm.v4i5.929
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.46729/ijstm.v4i5.929
Credit card abuse and fraud in credit card transactions pose a serious threat to financial companies and consumers. To overcome this problem, accurate and effective fraud detection is essential. In this study, we propose an approach that combines HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise), UMAP (Uniform Manifold Approximation and Projection), and SMOTE (Synthetic Minority Over-sampling Technique) methods to detect fraud in credit card transactions. The HDBSCAN method is used to group transactions based on their spatial density, allowing identification of suspicious groups of transactions. UMAP is used to reduce the dimension of transaction data, thus enabling better visualization and more efficient data analysis. In addition, we use SMOTE to overcome class imbalances, namely differences in the number of fraudulent and non-fraudulent transactions. In our experiments, we used. In this experiment, we used a dataset of credit card transactions that included both fraudulent and non-fraudulent transactions. The experimental results show that the proposed approach is able to detect fraud with high accuracy. The HDBSCAN method is able to effectively identify suspicious groups of transactions, while UMAP helps in better understanding and visualization of data. The use of SMOTE has successfully overcome class imbalances, resulting in more balanced fraud detection results between fraud and non-fraud. The results of this study show that the combination of HDBSCAN, UMAP, and SMOTE methods is effective in detecting fraud in credit card transactions. This approach can help financial companies identify suspicious transactions with high accuracy, reduce fraud losses, and improve the security of credit card transactions.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.46729/ijstm.v4i5.929
- https://ijstm.inarah.co.id/index.php/ijstm/article/download/929/815
- OA Status
- diamond
- Cited By
- 13
- References
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- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4391189458
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Raw OpenAlex JSON
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https://openalex.org/W4391189458Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.46729/ijstm.v4i5.929Digital Object Identifier
- Title
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Fraud Detection in Credit Card Transactions Using HDBSCAN, UMAP and SMOTE MethodsWork title
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articleOpenAlex work type
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enPrimary language
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2023Year of publication
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2023-09-26Full publication date if available
- Authors
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Rudy Setiawan, Budi Tjahjono, Gerry Firmansyah, Habibullah AkbarList of authors in order
- Landing page
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https://doi.org/10.46729/ijstm.v4i5.929Publisher landing page
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https://ijstm.inarah.co.id/index.php/ijstm/article/download/929/815Direct link to full text PDF
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://ijstm.inarah.co.id/index.php/ijstm/article/download/929/815Direct OA link when available
- Concepts
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Credit card, Credit card fraud, Computer science, Data mining, Computer security, Artificial intelligence, World Wide Web, PaymentTop concepts (fields/topics) attached by OpenAlex
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13Total citation count in OpenAlex
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2025: 10, 2024: 3Per-year citation counts (last 5 years)
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23Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.allowing | 80 |
| abstract_inverted_index.approach | 35, 155, 231 |
| abstract_inverted_index.balanced | 198 |
| abstract_inverted_index.combines | 37 |
| abstract_inverted_index.density, | 79 |
| abstract_inverted_index.enabling | 98 |
| abstract_inverted_index.identify | 171, 236 |
| abstract_inverted_index.included | 142 |
| abstract_inverted_index.overcome | 19, 112, 192 |
| abstract_inverted_index.problem, | 21 |
| abstract_inverted_index.proposed | 154 |
| abstract_inverted_index.security | 248 |
| abstract_inverted_index.accuracy, | 241 |
| abstract_inverted_index.accuracy. | 163 |
| abstract_inverted_index.addition, | 107 |
| abstract_inverted_index.analysis. | 105 |
| abstract_inverted_index.companies | 15, 235 |
| abstract_inverted_index.detecting | 224 |
| abstract_inverted_index.detection | 26, 200 |
| abstract_inverted_index.dimension | 93 |
| abstract_inverted_index.effective | 24, 222 |
| abstract_inverted_index.efficient | 103 |
| abstract_inverted_index.financial | 14, 234 |
| abstract_inverted_index.resulting | 195 |
| abstract_inverted_index.(Synthetic | 55 |
| abstract_inverted_index.Clustering | 42 |
| abstract_inverted_index.Technique) | 58 |
| abstract_inverted_index.consumers. | 17 |
| abstract_inverted_index.essential. | 28 |
| abstract_inverted_index.fraudulent | 121, 144 |
| abstract_inverted_index.non-fraud. | 205 |
| abstract_inverted_index.suspicious | 83, 172, 237 |
| abstract_inverted_index.combination | 214 |
| abstract_inverted_index.differences | 116 |
| abstract_inverted_index.effectively | 170 |
| abstract_inverted_index.experiment, | 132 |
| abstract_inverted_index.imbalances, | 114, 194 |
| abstract_inverted_index.transaction | 95 |
| abstract_inverted_index.Applications | 44 |
| abstract_inverted_index.Projection), | 52 |
| abstract_inverted_index.experimental | 149 |
| abstract_inverted_index.experiments, | 127 |
| abstract_inverted_index.successfully | 191 |
| abstract_inverted_index.transactions | 8, 74, 140, 238 |
| abstract_inverted_index.(Hierarchical | 39 |
| abstract_inverted_index.Approximation | 50 |
| abstract_inverted_index.Density-Based | 40 |
| abstract_inverted_index.Over-sampling | 57 |
| abstract_inverted_index.transactions, | 175 |
| abstract_inverted_index.transactions. | 66, 86, 124, 147, 229, 252 |
| abstract_inverted_index.understanding | 181 |
| abstract_inverted_index.visualization | 100, 183 |
| abstract_inverted_index.identification | 81 |
| abstract_inverted_index.non-fraudulent | 123, 146 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 96 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.7699999809265137 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile.value | 0.92164196 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |