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.
