(24 hrs) AI-Powered Finance: Automating Insights & Risk Management (Part of SCTP)
About This Course
This course equips business owners, finance professionals, and individuals seeking to upskill with the practical capability to deploy Generative and Agentic AI as "cognitive thinking partners" across key financial functions.
Participants will develop a structured understanding of generative and discriminative models, mastering the transition from traditional "Transactional Finance" to a "Responsible Augmented" framework.
Through high-stakes scenarios and the Finance AI Lab, participants will explore how AI agents can be orchestrated to automate complex workflows including real-time PayNow/PINT (Peppol) reconciliations, IRAS GST validation, and Zero-Day Close operations.
The course emphasizes advanced prompt engineering to automate BEPS 2.0 tax documentation, ISSB-aligned ESG reporting, and SGX-compliant board disclosures.
Participants will secure financial integrity by deploying AI to detect deepfake fraud, analyze circular ownership via ACRA synthesis, and predict risks using regional "soft data".
The curriculum is deeply anchored in the 2026 MAS AI Risk Management Handbook, teaching "Human-in-the-Loop" (HITL) oversight to mitigate model bias and ensure compliance with the 17 core regulatory considerations.
Ethical and societal implications, such as the "black box" nature of expense management and the strategic upskilling of the workforce, are analyzed to ensure transparency and fairness.
By the end of the course, you will be equipped to build a concrete Finance Augmentation Backlog, moving beyond simple automation to drive measurable ROI and strategic innovation in finance operations.
What You'll Learn
2. Utilise generative AI models to produce outputs from training data and algorithms for automating accounting workflows such as summarisation, transformation, reasoning, and content augmentation.
3. Evaluate how prompt engineering influences generative AI outputs by testing prompt variations and analysing resulting performance and fitness-for-purpose for financial insights.
4. Assess how data quality and model-pipeline factors can introduce limitations or bias in AI outputs by analysing risks to compliance and the integrity of financial decision-making.
5. Propose practical generative AI applications for finance by applying core principles of generative AI, while identifying ethical and societal implications and incorporating these considerations into the proposed solution.
Entry Requirements
Prerequisites
• Experience with finance and accounting: Minimal one year of work experience with knowledge of finance and accounting.
Language:
• Attained at least WPLN level 6 OR
• Obtained Grade C6 for GCE O level English OR
• Other equivalent qualifications
Academic:
• Obtained at least a pass or C6 at GCE O Level in at least 3 subjects OR
• Candidates with other qualifications will be considered on a case-by-case basis OR
• Mature candidates (≥ 30 years old) with 8 years of relevant working experience