Use of advanced data analytics for financial fraud detection
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The study analyzes the use of advanced data analysis techniques as a central strategy in the detection of financial fraud, in a context marked by the increasing complexity of digital transactions and the sophistication of illicit mechanisms. Through an exploratory review of specialized literature published in high-impact databases, supervised learning methods, unsupervised learning methods and hybrid approaches were identified and compared, evaluating their effectiveness, limitations and implementation potential. The results show that supervised algorithms, such as neural networks and random forests, offer high accuracy when labeled data is available, while unsupervised techniques stand out for their ability to identify emerging frauds without prior information. The research concludes that the combination of both approaches using ensemble models constitutes a promising alternative, although it involves technical, operational and ethical challenges. The importance of articulating these methodologies with robust infrastructures and regulatory frameworks that guarantee transparency and explainability is highlighted.
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