Artificial intelligence in Risk Management
Artificial intelligence in Risk Management
Risk management is a fundamental component of any organization. Faced with multiple potential threats, it is essential to put in place effective tools and approaches to identify, evaluate and mitigate them. The financial industry, in particular, is characterized by increased complexity and volatility. In this changing context, the effectiveness of risk management practices becomes a determining factor for the sustainability of financial institutions.
Traditional risk management methods have many limitations, such as their inability to manage huge amounts of data and respond quickly to market fluctuations. With the advent of artificial intelligence (AI), new opportunities have opened up to improve risk management through advanced predictive analytics and process automation. Artificial intelligence makes it possible to analyze massive volumes of data in real time, thus providing increased responsiveness to rapid changes in the financial market. Artificial intelligence is revolutionizing risk management in the financial sector. Using deep learning, machine learning algorithms and natural language processing, it offers powerful tools to improve decision-making, reduce risk and increase overall financial stability. Machine learning models can detect complex patterns and predict trends that would otherwise be imperceptible with traditional methods. This ability to anticipate risks before they materialize is crucial to maintaining the resilience of financial institutions.
This thesis aims to explore the application of AI to risk management, focusing on machine learning and deep learning techniques, in order to develop innovative solutions to predict and manage various types of risks. The objective is to offer financial institutions a better understanding of the risks they face and to provide them with the necessary tools to control them proactively and effectively. By contributing to the advancement of knowledge in the field of artificial intelligence, this research proposes innovative approaches that promise to significantly improve risk management practices in the financial sector.
Artificial Intelligence (AI); Risk management ; Machine learning; Deep learning; Predictive modeling; Financial industry; Financial stability; Decision making.
Bac+5 in applied mathematics, computer engineering or finance.
Thesis director : Pre. Sofia Ayouche
Thesis co-supervisor : Pre. Ahlam Mohammadi
Please send the application before November 19, 2024 to:
Cedoc.admission@ueuromed.org & s.ayouche@ueuromed.org