Please use this identifier to cite or link to this item: http://hdl.handle.net/10609/148477
Title: Crimen Financiero. Detección de fraude en tarjetas de crédito aplicando aprendizaje automático
Author: Artola Moreno, Álvaro
Tutor: Molina Casasnovas, Rubén
Others: Monzo, Carlos  
Keywords: machine learning
classification
artificial intelligence
regression
debit
credit
Issue Date: Jun-2023
Publisher: Universitat Oberta de Catalunya (UOC)
Abstract: The goal of this project is to implement predictive models utilising machine learning techniques in order to prevent financial fraud in transactions made using credit and debit cards. The vast amount of data available for analysis presents an opportunity to construct predictive models which can detect anomalous patterns and behaviours in transactions. Several machine learning techniques have been evaluated, including logistic regression analysis, decision trees, neural networks, and k-nearest neighbour classification, with an emphasis on their ability to handle large volumes of data and adapt to various types of models. The results of this project are highly promising, as the implemented predictive models have demonstrated a high degree of accuracy in detecting suspicious anomalous transactions. Early identification of fraudulent activities can be of great benefit to financial institutions, enabling them to take swift action to prevent economic losses and safeguard their clients from potential fraudulent attacks.
Language: Spanish
URI: http://hdl.handle.net/10609/148477
Appears in Collections:Bachelor thesis, research projects, etc.

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