Explore how AI transforms finance in this tech-forward module. Learn AI-powered forecasting, fraud detection, algorithmic trading, and FinTech innovations like blockchain and robo-advisors. Build skills for future-ready financial decision-making.
Overview
This specialized module examines how Artificial Intelligence is applied in the world of business finance and financial services. Building on basic finance concepts, students will see how AI and machine learning enhance financial decision-making and operations. Topics include AI in financial analysis (like using algorithms to forecast sales or optimize budgets), in investments (such as algorithmic trading and robo-advisors), in risk management (fraud detection systems, credit scoring models), and in customer-facing financial services (chatbots in banking, personalized financial products). Students also gain an understanding of the broader FinTech landscape, exploring innovations like digital payments, blockchain, and how they disrupt traditional financial models. By blending finance fundamentals with technology insights, this module prepares students to navigate the rapidly evolving financial sector where analytical skills and tech-savvy go hand in hand.
The Details
FOR SKILLSFUTURE CLAIMS
Registered Title: (AI Application in Financial for Business
(Classroom & Asynchronous))
TPGateway Code:
total hours
32:00
Mode of Learning
On Campus
Curriculum
Course Duration
SSG Approved Training & Assessments Hours:32:00Trainer Facilitated Hours (On Campus/Virtual):23:00Self-Directed E-learning Hours:04:00Assessment Hours:05:00
Programme Objectives
Upon completion of this module, students will be able to:
Understand core principles of business finance, including financial planning, investment decision criteria, and risk management techniques used by companies.
Identify key applications of AI in finance, explaining how technologies are used for tasks such as financial forecasting, portfolio management, fraud detection, and improving customer experience in banking and finance.
Analyze financial data using AI-driven tools, interpreting outputs from predictive models or analytics software to support budgeting, forecasting, or investment decisions.
Discuss FinTech innovations (e.g., mobile payments, blockchain, cryptocurrency) and evaluate their impact on traditional business finance operations and strategies.
Recognize ethical and regulatory considerations when implementing AI in finance, such as data security, privacy, and compliance with financial regulations.
Course Topics
Business Finance Fundamentals
Learning Outcome: Recap of essential finance concepts – time value of money, basics of capital budgeting (e.g., NPV, ROI), financial planning, and working capital management.
Financial Markets Overview
Learning Outcome: Understanding stocks, bonds, and other financial instruments; introduction to how businesses raise capital (equity vs debt financing) – providing context for where AI might be applied (e.g., trading, credit analysis).
AI in Financial Analysis
Learning Outcome: Using AI for forecasting and analytics – for instance, machine learning models that predict sales trends, optimize pricing, or forecast demand; tools for big data analysis in finance (like handling large financial datasets for trend analysis).
Automated Trading and Investment
Learning Outcome: Introduction to algorithmic trading, how robo-advisors work for personal investing, and AI in portfolio management (e.g., asset allocation algorithms). Also, risk management via AI – credit scoring systems for loans, fraud detection in transactions using pattern recognition.
FinTech and Digital Banking
Learning Outcome: Exploration of financial technology innovations – digital payment platforms (PayPal, mobile wallets), peer-to-peer lending, crowdfunding, blockchain technology basics and its uses (crypto, smart contracts), and how these disrupt traditional finance.
AI in Financial Services Operation
Learning Outcome: Examples such as chatbots for customer service in banks, AI for regulatory compliance (RegTech), and intelligent financial reporting systems.
Ethics and Regulation
Learning Outcome: Issues like algorithmic bias in lending, data privacy in financial data handling, cybersecurity in fintech, and overview of how regulators are responding to AI (guidelines for AI in credit decisions, etc.).
Case Studies
Learning Outcome: Real-world examples of companies implementing AI in their finance departments or financial startups leading with AI, to illustrate challenges and successes.
To enrol, you must fulfil the following requirements
1
AGED 16 YEARS AND ABOVE
and
2
GCE ‘O’ LEVEL CREDITS WITH MINIMUM GRADE C6 FOR 3 SUBJECTS, INCLUDING ENGLISH AND MATHEMATICS;
OR
3
WSQ ADVANCED CERTIFICATE IN ANY FIELD;
OR
4
ADVANCED CERTIFICATE IN A BUSINESS MANAGEMENT – RELATED FIELD (E.G., ACCOUNTING, PROJECT MANAGEMENT, HUMAN RESOURCES, ETC.) FROM ANY PRIVATE EDUCATION INSTITUTION (PEI);
OR
5
HIGHER NITEC
and
6
ALL STUDENTS ARE REQUIRED TO HAVE BASIC KNOWLEDGE OF COMPUTER AND MICROSOFT OFFICE.
Graduation
To graduate, you must fulfil the following requirements
1
International Students
To enrol, you must fulfil the following requirements
1
AGED 16 YEARS AND ABOVE
and
2
FORMAL INTERNATIONAL YEAR 10 EDUCATION;
OR
3
QUALIFICATIONS FROM PROFESSIONAL INSTITUTES MAY BE CONSIDERED ON A CASE-BY-CASE BASIS;
and
4
PASS TEMBUSU ENTRANCE TEST
and
5
English Requirements: Possess a minimum credit in English at GCE ‘O’ Level or its equivalent OR IELTS score 5.5 or TOEFL score 46
OR
6
PASS TEMBUSU'S ENGLISH TEST
and
7
ALL STUDENTS ARE REQUIRED TO HAVE BASIC KNOWLEDGE OF COMPUTER AND MICROSOFT OFFICE.
Graduation
To enrol, you must fulfil the following requirements
