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dc.contributorBarrado Muxí, Cristina
dc.contributor.authorTrujillo Martín, Judith
dc.contributor.otherUniversitat Politècnica de Catalunya. Arquitectura de Computadors
dc.date.accessioned2019-02-21T21:49:40Z
dc.date.available2019-02-21T21:49:40Z
dc.date.issued2019-02-07
dc.identifier.urihttp://hdl.handle.net/2117/129534
dc.description.abstractFinancial entities are increasingly looking for new technologies in order to improve their services and increase their profits. One of the challenges raised by a well-known financial entity is to reduce doubtful credit from the customers' transactions (face-to-face purchases). When transactions are unpaid by customers, the financial entity has to afford it and the corresponding amount of money is added to the doubtful credit. These transactions are accepted due to incorrect validation during the authorization. When the financial entity has connectivity problems and it is not able to validate a transaction, it allows a payment processor entity to validate the transaction. The payment processor entity does not have the legal permission to check if the customer has enough credit to assume the transaction. Therefore, the payment processor entity sometimes validates transactions that the customer will not pay. This project proposes to implement an algorithm able to predict which transactions will be paid by the customer and which not. This algorithm has to be executed by the payment processor entity. This project proposes to implement an algorithm able to predict which transactions will be paid by the customer and which not. Predictions are made without information about the available credit that the customer has. The algorithm has to be executed by the payment processor entity. After testing several models, it is concluded that the Random Forest algorithm trained with balancing techniques can predict 57.6% of all the transactions that will not be paid by customers. All with a certainty of 97.3%.
dc.language.isoeng
dc.publisherUniversitat Politècnica de Catalunya
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Enginyeria de la telecomunicació
dc.subject.lcshMachine learning
dc.subject.lcshElectronic funds transfers
dc.subject.otherData analytics
dc.subject.otherMachine learning
dc.subject.otherData mining
dc.subject.otherSupervised learning
dc.subject.otherUnpaid transactions
dc.titleMachine learning applied to detection and prevention of unpaid transactions
dc.typeBachelor thesis
dc.subject.lemacAprenentatge automàtic
dc.subject.lemacMoneda electrònica
dc.rights.accessOpen Access
dc.date.updated2019-02-21T04:55:15Z
dc.audience.educationlevelEstudis de primer/segon cicle
dc.audience.mediatorEscola d'Enginyeria de Telecomunicació i Aeroespacial de Castelldefels


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