Machine Learning-Driven Fraud Detection: Management, Compliance, and Integration
DOI:
https://doi.org/10.5281/zenodo.14064121ARK:
https://n2t.net/ark:/40704/AJSM.v2n6a02Disciplines:
EconomicsSubjects:
Financial Risk ManagementReferences:
20Keywords:
Fraud Detection, Data Compliance, Machine Learning, Manasgement SystemAbstract
This research delves into the comprehensive methodology of employing machine learning in the domain of fraud detection, outlining the critical steps from data collection to continuous learning. It emphasizes the importance of adhering to data protection regulations during the data collection phase and the significance of preprocessing in preparing the data for analysis. The study explores various machine learning models, including supervised and unsupervised learning techniques, and evaluates their performance using metrics such as accuracy and AUC-ROC. It highlights the necessity of continuous learning to adapt to evolving fraud tactics and the challenges of integrating machine learning models into existing fraud detection systems. Ultimately, this research underscores the transformative potential of machine learning in enhancing the accuracy and efficiency of fraud detection, safeguarding financial transactions, and protecting consumers from fraudulent activities.
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This work is licensed under a Creative Commons Attribution 4.0 International License.
