Financial Applications of Artificial Intelligence (Synchronous e-Learning)
About This Course
This course provides a complete understanding of financial sector applications of Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and Neural Networks (NN), uniquely designed for those interested in the intersection of technology and finance. Learners will explore the evolution of AI, its applications, and its transformative impact , with a special emphasis on the financial sector. Learners will be introduced to AI tools which can be applied to optimize financial processes such as financial ratio analysis, financial fraud detection and investment analysis.
Leveraging financial industry insights, this course equips learners with the expertise to identify potential AI applications and navigate challenges within the rapidly evolving digital finance landscape. By the end of this module, learners be able to understand the use of AI tools in financial functions such as financial forecasting, capital market analysis and determining financial investment strategies.
This course forms part of the SCTP NUS Digital Finance Project Management with AI which consists of 8 modules.
What You'll Learn
•Understand the historical development of Artificial Intelligence (AI) and its impact on the financial industry
•Compare the effectiveness of different Machine Learning (ML) (supervised, unsupervised, and reinforcement learning) in the Fintech sector. Gain a comprehensive understanding of Machine Learning (ML) concepts with examples of their use in different sectors, particularly in the Fintech sector such as financial fraud detection, algorithmic trading, etc .
•Identify the role of neural networks in deep learning applications in the financial sector eg credit risk assessment, financial forecasting
•Understand advanced neural network architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) networks, and understand their applications in the context of digital finance.
•Recognize the use of neural networks in complex financial tasks, such as sentiment analysis and text summarization of financial reports, in terms of their efficiency and accuracy in driving financial decision-making.
Entry Requirements
General Diploma