Mobile money transaction fraud detection using machine learning algorithms / Johnaaron R. Bustonera [and three others].

By: Contributor(s): Material type: TextTextPublication details: Manila : National University, 2021.Description: 118 leaves : color illustrations ; 28 cmSubject(s): LOC classification:
  • UGT COE BSCpE .B87 2021
Contents:
Chapter 1. The Problem and its background -- Chapter 2. Literature Review and Framework of the Projec 3. Project Design and Methodology -- Chapter 4. Results and Discussions -- Chapter 5. Summary, Conclusion and Recommendation -- References.
Summary: The detection of fraudulent behaviors in mobile money transfers is explored and contrasted using three modern machine learning models: Light Gradient Boost, Random Forests and XGBoost.
Item type: Thesis
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Holdings
Item type Current library Home library Collection Call number Copy number Status Date due Barcode
Thesis Thesis National University - Manila LRC - Main Thesis Computer Engineering UGT COE BSCpE .B87 2021 (Browse shelf(Opens below)) c.1 Available UGTHE000002864

Includes bibliographical references.

Chapter 1. The Problem and its background -- Chapter 2. Literature Review and Framework of the Projec 3. Project Design and Methodology -- Chapter 4. Results and Discussions -- Chapter 5. Summary, Conclusion and Recommendation -- References.

The detection of fraudulent behaviors in mobile money transfers is explored and contrasted using three modern machine learning models: Light Gradient Boost, Random Forests and XGBoost.

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