Big Data-Driven Fraud Detection Using Machine Learning and Real-Time Stream Processing
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912410774798336 |
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| author | Liu, Chen Tang, Hengyu Yang, Zhixiao Zhou, Ke Cha, Sangwhan |
| author_facet | Liu, Chen Tang, Hengyu Yang, Zhixiao Zhou, Ke Cha, Sangwhan |
| contents | In the age of digital finance, detecting fraudulent transactions and money laundering is critical for financial institutions. This paper presents a scalable and efficient solution using Big Data tools and machine learning models. We utilize realtime data streaming platforms like Apache Kafka and Flink, distributed processing frameworks such as Apache Spark, and cloud storage services AWS S3 and RDS. A synthetic dataset representing real-world Anti-Money Laundering (AML) challenges is employed to build a binary classification model. Logistic Regression, Decision Tree, and Random Forest are trained and evaluated using engineered features. Our system demonstrates over 99% classification accuracy, illustrating the power of combining Big Data architectures with machine learning to tackle fraud. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_02008 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Big Data-Driven Fraud Detection Using Machine Learning and Real-Time Stream Processing Liu, Chen Tang, Hengyu Yang, Zhixiao Zhou, Ke Cha, Sangwhan Distributed, Parallel, and Cluster Computing In the age of digital finance, detecting fraudulent transactions and money laundering is critical for financial institutions. This paper presents a scalable and efficient solution using Big Data tools and machine learning models. We utilize realtime data streaming platforms like Apache Kafka and Flink, distributed processing frameworks such as Apache Spark, and cloud storage services AWS S3 and RDS. A synthetic dataset representing real-world Anti-Money Laundering (AML) challenges is employed to build a binary classification model. Logistic Regression, Decision Tree, and Random Forest are trained and evaluated using engineered features. Our system demonstrates over 99% classification accuracy, illustrating the power of combining Big Data architectures with machine learning to tackle fraud. |
| title | Big Data-Driven Fraud Detection Using Machine Learning and Real-Time Stream Processing |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2506.02008 |