AI in Finance: Delivering Business Value

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AI in Finance: Delivering Business Value

Programme Fee (S$)	Language	Duration	Effort
1500	English	6 weeks	4–6 hours per week

Course Overview

In the rapidly-evolving landscape of finance, artificial intelligence (AI) is no longer just an innovative concept but a transformative reality. The integration of AI in finance, through advanced technologies like process automation, machine learning, and predictive analytics, is reshaping the industry’s future. To stay ahead in this dynamic environment, professionals need more than a cursory understanding of AI; they require specialised skills to effectively implement AI solutions within their businesses.

Recognising this need, the National University of Singapore (NUS) Advanced Computing for Executives (ACE) has designed the online short course, AI in Finance: Delivering Business Value. This course is tailored for those looking to master the design, development and deployment of AI solutions in the financial sector. The course’s core objective is to equip participants with a fundamental understanding of AI technology, enabling them to identify and add value to their organisations through strategic AI implementations.

Spanning six weeks, this National University of Singapore AI course delves deep into the essence of AI and data science, particularly within the financial domain. The curriculum covers the planning and management of AI project risks, with a special focus on ethical considerations and regulatory compliance — a critical aspect of AI for finance. The course also provides a comprehensive exploration of AI principles in a financial business context, preparing participants to anticipate and leverage future AI trends.

What sets this course apart is its non-technical approach, making it accessible to decision-makers and business leaders. It is specifically crafted to empower those at the helm of business strategy with a deeper understanding of AI’s potential. The practical orientation of the course highlights how AI solutions can create tangible business value. Participants will also learn the nuances of commercialising AI products, ensuring they are well-versed in the ethical and regulatory aspects essential for AI in finance.

For executives and business leaders eager to harness the power of AI in the financial sector, the NUS AI course offers an invaluable opportunity. It is an investment in understanding how to navigate the complex yet rewarding world of artificial intelligence, driving innovation and growth in their respective organisations.

Course Curriculum

Orientation Module Welcome to your Online Campus
Meet and engage with your learning and peer networks as you navigate the Online Campus.

Module 1 AI and Data Science
Learn how to frame the right questions about the application of AI in finance.

Module 2 The Application of AI in Finance
Explore how AI is currently being implemented in finance.

Module 3 Planning and Managing AI in Finance Projects
Explore how to plan and manage AI in finance projects in terms of design, development and implementation of projects to deliver higher performance.

Module 4 Managing AI Pitfalls, Ethical Considerations and Regulatory Compliance
Explore the ethical and regulatory compliance challenges for AI projects.

Module 5 Managing AI Innovation in Finance
Learn how to use AI innovation in finance, from creation, protection and commercialisation of AI-enabled products and services.

Module 6 The Future of AI in Finance: Management Perspectives
Explore how organisations can prepare strategically and operationally for the future of AI in finance.

Instructors

Mr Sarat Mohanty

Mr Zaid Bin Hamzah

Career Opportunities

Artificial Intelligence (AI) is increasingly being integrated into the finance sector, creating new career opportunities. Some of the roles that can be pursued in AI for finance include:

AI engineers: Develop and implement AI algorithms and models to improve financial processes and decision-making.
Data scientists: Analyse large datasets to uncover patterns and trends that can inform financial strategies and decisions.
Machine learning engineers: Design and implement machine learning models to automate financial tasks and improve decision-making.
Data analysts: Analyse data to provide insights and support financial strategies and decisions.
Financial engineers: Apply engineering principles to financial problems, using AI and other advanced mathematical techniques.
Risk managers: Use AI to assess and manage financial risks, improving the accuracy and efficiency of risk assessment processes.
Investment managers: Leverage AI algorithms to identify potential investment opportunities and optimise investment strategies.
Financial planners: Utilise AI tools to create personalised financial plans for clients, streamlining the planning process and providing more tailored advice.