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Machine learning based approaches for intelligent adaptation and prediction in banking business processes. (c2018)

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dc.contributor.author Tay, Bilal M.
dc.date.accessioned 2019-04-16T07:35:27Z
dc.date.available 2019-04-16T07:35:27Z
dc.date.copyright 2018 en_US
dc.date.submitted 2018-12-20
dc.identifier.uri http://hdl.handle.net/10725/10451
dc.description.abstract Companies, nowadays, rely on systems and applications to automate their business processes and data management. In this context, the notion of integrating machine learning techniques in banking business processes has emerged, where trainable computational algorithms can be improved by learning. Our objective in this work is to propose machine learning models that can benefit from the historical data available in banking environment in order to improve and automate their business processes. In this context, we first propose in this thesis a model providing Intelligent Behavior- Aware Adaptation of Roles using Machine Learning Classification. The proposed scheme is capable of assessing the deployed access control polices and updating them systematically with new roles based on employees behaviors and system constraints. Experiments on real life data set explore the feasibility of our approach, which also provides better performance in terms of required authorizations, transactions time and employees working hours. Moreover, we propose in this thesis a Deep Learning Based Approach to Predict Non-Performing Loans. Compared to the literature, the proposed model embeds a new feature selection method and offers higher detection accuracy, which helps lenders and financial institutions to better manage their lending activities and loan monitoring processes. en_US
dc.language.iso en en_US
dc.subject Lebanese American University -- Dissertations en_US
dc.subject Dissertations, Academic en_US
dc.subject Machine learning en_US
dc.subject Banks and banking -- Automation en_US
dc.subject Bank employees -- Classification en_US
dc.subject Bank loans -- Data processing en_US
dc.subject Uncollectible accounts en_US
dc.title Machine learning based approaches for intelligent adaptation and prediction in banking business processes. (c2018) en_US
dc.type Thesis en_US
dc.term.submitted Fall en_US
dc.author.degree MS in Computer Science en_US
dc.author.school SAS en_US
dc.author.idnumber 200302214 en_US
dc.author.commembers Haraty, Ramzi
dc.author.commembers Touma, Rony
dc.author.department Computer Science And Mathematics en_US
dc.description.embargo N/A en_US
dc.description.physdesc 1 hard copy: xi, 73 leaves; col. ill.; 30 cm. available at RNL. en_US
dc.author.advisor Mourad, Azzam
dc.keywords Machine Learning en_US
dc.keywords Deep Learning en_US
dc.keywords Role Engineering en_US
dc.keywords Logistic Regression en_US
dc.keywords Intelligent Role Adaptation en_US
dc.keywords Behavior-Aware Role Adaptation en_US
dc.keywords Banking Business Process en_US
dc.keywords Social Lending en_US
dc.keywords Feature Selection en_US
dc.keywords Non Performing Loans en_US
dc.keywords Prediction en_US
dc.keywords Role Based Access Control en_US
dc.description.bibliographiccitations Bibliography: leaves 65-73. en_US
dc.identifier.doi https://doi.org/10.26756/th.2019.107 en_US
dc.author.email bilal.tay@lau.edu en_US
dc.identifier.tou http://libraries.lau.edu.lb/research/laur/terms-of-use/thesis.php en_US
dc.publisher.institution Lebanese American University en_US
dc.author.affiliation Lebanese American University en_US


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