NTU FlexiMasters in Business and Financial Analytics

90% SkillsFuture Funding

Significantly lowers financial barriers, making elite postgraduate education accessible and low-risk.

Postgraduate analytics curriculum

Builds immediate, job-relevant capabilities in AI, finance, and business decision-making.

Stackable MSc credits

Protects long-term ROI by allowing learners to progress seamlessly into a full Master’s degree.

Flexible format

Enables working professionals to upskill without quitting their jobs or disrupting careers.

Industry-aligned modules

Ensures skills are applicable to real business, finance, and digital transformation challenges.

Peer & alumni exposure

Expands professional perspective and career opportunities through cross-industry learning.

For more information on the intake dates, trainers and programme fee, please click here.

Module	Objective
AI and Analytics Technologies in Enterprise	Analytics, machine learning, and Artificial Intelligence (AI) are critical building blocks in value creation for any organization or industry, yet managing them is a complex endeavour. This presents huge opportunities to harness the power of analytics and machine learning, explore and exploit business problems, and turn them into solutions. This course introduces the concept of analytics with machine learning and how a business can embed and embrace them in its operations. Learning Outcomes: Understand the basics of analytics and machine learning and how they are relevant to business. Understand the elements of machine learning and the steps in developing machine learning capability. Construct a machine learning solution for a given problem. Manage the use of analytics and machine learning to support business decisions.
AI and Analytics in Finance, Credit and Related Risks	In today’s fast-changing financial landscape, businesses must navigate growing risks while leveraging data-driven insights for smarter decision-making. This program equips professionals with the knowledge and tools to assess, mitigate, and manage financial and credit risks effectively, integrating AI-powered analytics to enhance decision-making and operational efficiency. Participants will deeply explore financial and credit risk landscapes, from market volatility, regulatory compliance, and stress testing to credit scoring models and portfolio strategies. The course emphasizes real-world applications, helping professionals apply quantitative techniques, AI-driven insights, and automation to streamline risk assessment and improve financial resilience. As the course progresses, participants will dive into AI-driven analytics, machine learning applications, and generative AI in finance. Through hands-on workshops, they will gain practical experience with AI and machine learning software tools for financial modeling, such as predictive risk assessment, credit evaluation, customer profiling, data synthesis and simulation. These interactive exercises will equip participants with the skills to effectively integrate AI-driven innovations into financial decision-making and risk management. Learning Outcomes Interpret fundamental financial risk concepts, their interdependencies, and regulatory considerations, evaluating how data-driven approaches enhance risk management. Apply credit risk assessment principles and quantitative techniques to analyze exposures, market dynamics, and decision-making in financial contexts. Analyze risk mitigation strategies, portfolio management techniques, and scoring models to optimize financial risk management. Design AI and data analytics solutions for financial modeling, decision-making, and automation, while considering ethical, regulatory, and operational implications.
Data Management and Visualization	Data has become integral to businesses and organizations. This course is built to bridge the gap between market talent demand and talent availability, specifically in business data management and business visualization. The course serves dual roles of presenting the importance of data on business decision-making and of demonstrating ways that visualization can effectively augment business decisions. Learning Outcomes Equipped with skills in solving data-related problems and designing visualizations to augment business decision-making. Equipped with strong conceptual and technical knowledge in database designing and implementation and developing visualization relevant to business. Understand how databases support business processes and gather information for business analytics. Apply knowledge and skills to analyze a variety of business processes, identify data requirements, and create corresponding visualization strategies.
Design Thinking, Blockchain Technology Management and Cybersecurity	Design thinking has become increasingly popular in the world of business, with small and medium-sized to large firms like P&G and SAP using and advocating it. Design thinking, emphasizing human-centred design and empathy-driven solutions, has been promoted widely by design firms like IDEO and Stanford University’s D School. It has now become a must-know tool for companies that are keen to grow and flourish through continuous innovation. The course will introduce you to the key principles of design thinking, utilizing relevant case studies. It is also crucial for you to appreciate the core technical aspects of blockchain, especially when it promises to disrupt the current business landscape through consensus-driven decentralization of the traditional computing fabric. Concepts of blockchain will be discussed, examining the blockchain using cases and challenges related to its implementation. With the proliferation of information and communication technology products and services, it is of paramount importance for everyone to understand the role of security and privacy in practice. Financial technology, data analytics, and digital transformation of businesses have immensely amplified the value of data, thereby demanding the appreciation of cybersecurity threats and privacy issues in every sphere of business. Learning Outcomes Understand the key innovation skills necessary for leading firms Apply fundamental concepts and principles of design thinking to the analytics domain Identify business opportunities in the AI and analytics domain Generate business models to commercialize business opportunities in the AI and analytics domain Understand challenges associated with commercializing AI technologies Introduction to the evolving blockchain ecosystem Understand the underlying technological elements and workings of blockchain, use cases of blockchain, and challenges with using blockchain Understand the possible use cases of blockchain Acquainted with the cybersecurity risks in the business sphere Understand the security and privacy mechanisms that can protect invaluable data in storage, transit, access and usage.