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AI-Powered Data Analytics for Finance & Accounting: Turning Numbers into Insights

This practical, business-focused course equips Finance and Accounting professionals with the skills to leverage AI for data analysis, trend forecasting, reporting automation, and smarter decision-making — without requiring technical or coding experience. Participants will learn how AI transforms raw financial data into actionable insights that drive accuracy, efficiency, and strategic value.

Objective

By the end of this course, participants will be able to:

• Understand fundamental AI concepts and conversational AI tools
• Apply the 4 stages of data analysis to financial data
• Use AI assistants to analyze sales, inventory, cash flow, bank reconciliation, and activity-based costing
• Generate actionable insights from financial data using AI

Outline

Module 1: Introduction to AI

1.1 What is Artificial Intelligence?
• Definition and core concepts
• Natural Language Processing (NLP) basics
• Large Language Models (LLMs) explained

1.2 Conversational AI Tools for Finance Professionals
• ChatGPT (OpenAI): Strengths in explanation and code generation
• Claude (Anthropic): Excel at analysis and structured outputs
• Gemini (Google): Integration with Google Workspace
• DeepSeek: Cost-effective alternative for technical tasks
• Comparison matrix: Use cases, strengths, and limitations

1.3 AI Capabilities and Limitations in Finance
• What AI can do and cannot do
• Ethical considerations and data privacy
• Best practices for prompting AI tools

Module 2: The 4 Stages of Data Analysis

2.1 Stage 1: Descriptive Analytics - "What Happened?"
• Understanding historical data
• Key metrics and KPIs in finance
• Using AI to summarize financial statements
• Generating reports and dashboards descriptions

2.2 Stage 2: Diagnostic Analytics - "Why Did It Happen?"
• Root cause analysis techniques
• Variance analysis
• Trend identification
• Using AI to identify anomalies and outliers

2.3 Stage 3: Predictive Analytics - "What Will Happen?"
• Forecasting fundamentals
• Time series analysis basics
• Using AI to build simple prediction models
• Limitations of AI predictions in finance

2.4 Stage 4: Prescriptive Analytics - "What Should We Do?"
• Scenario planning
• Optimization techniques
• Decision support
• Using AI for recommendation generation

Activity: Group exercise applying all 4 stages to a sample financial dataset

Module 3: Data Preparation and AI Prompting

3.1 Preparing Financial Data for AI Analysis
• Data cleaning fundamentals
• Structuring data for AI tools
• Common data quality issues
• CSV and Excel best practices

3.2 Effective Prompt Engineering for Finance
• Anatomy of a good prompt
• Providing context and constraints
• Iterative refinement techniques

3.3 Hands-on Practice
• Writing prompts for financial analysis
• Testing different AI tools
• Comparing outputs and accuracy

Module 4: AI Applications in Finance & Accounting

4.1 Sales Analysis with AI
• Analyzing sales by product, region, and time period
• Identifying top and bottom performers
• Detecting unusual sales patterns
• Generating sales forecasts

4.2 Inventory Analysis with AI
• Calculating inventory metrics automatically
• Identifying slow-moving or obsolete inventory
• Optimizing reorder quantities
• Forecasting inventory requirements

4.3 Cash Flow Analysis with AI
• Cash flow statement preparation assistance
• Trend analysis and forecasting
• Identifying cash flow improvement opportunities
• Scenario analysis for cash management

4.4 Bank Reconciliation with AI
AI Applications:
• Matching transactions between files
• Identifying unreconciled items

Module 5: Generating Actionable Insights

5.1 AI-Enhanced Insight Generation
• Using AI to synthesize multiple analyses
• Generating executive summaries
• Creating presentation content
• Identifying strategic recommendations

Who should attend

for professionals in finance and accounting who want to leverage AI and data analytics to improve decision-making, accuracy, and productivity,

Methodology

- Short conceptual teaching
- Guided hands-on practice
- Real finance-based datasets
- Group discussion and problem-solving
- Case studies from real business scenarios

Testimonials

Profile of Jason Khoo

Jason Khoo is a Microsoft Certified Trainer (MCT) and an ACTA-certified trainer with over 20 years of training experience. A Microsoft Certified Power BI Data Analyst Associate, he brings more than 30 years of hands-on experience in data analytics and business intelligence. He is also the Author of 4 Amazon books.

His expertise spans a wide range of areas, including Artificial Intelligence (AI), Data Science, Generative AI, Machine Learning, Digitalization, Predictive Analytics, Interactive Dashboard Design, Data Storytelling, and Data Visualization.

He is also highly skilled in developing automated, real-time reports and conducting data and visual analytics using tools such as Microsoft Excel, Power BI, and Tableau.

Throughout his career, Jason has held key roles at organizations such as Tibs (now SMRT), MobileOne Ltd, 3M, and Virgin Mobile.

During his tenure, he has worked with multiple databases, extracted and downloaded information from systems and run numerous analysis. These analyses spanned across many departments such as Finance, Sales, Marketing, Human Resources, Payroll, etc.

After his employment, he worked as a data analytics consultant for many companies, including Discovery Asia, 3M, Tanah Merah Country Club, Johnson and Johnson Vision Care, National Environmental Agency, Timberland, etc.
His data analytics skills combined with Excel allowed him to deliver many reports to the clients without them having to incur software cost. These reports include Dashboards, Top 10 charts, Risk Analysis, Business Models, KPIs, etc.

As a trainer, he is passionate about transferring his knowledge and imparting his skills to his participants. To day, he has trained thousands in his face to face workshops and had conducted talks for thousands as well.

Trans-Island Bus Services Ltd (Tibs) –Operations and Revenue Analytics, Passengers Travelling Behaviour Analytics
• Daily tracking of revenue,
• Prepare KPI for LTA,
• Fare revision analysis

Mobile One Asia Ltd (M1) – Financial analytics, Customer Call Usage Analytics
• Full company budgeting and business planning,
• Monthly Analysis,
• KPI reporting

3M – Sales, Cost and Inventory analytics
• Daily and Monthly Sales Forecasting,
• Analysis of Marketing Budget,
• Analysis of Inventory and shipments.
• Cost Analysis for new products (BOM analysis)

Virgin Mobile – Retail, Marketing, Payroll, Admin, IT, Finance Analytics
• Daily KPI reporting,
• Monthly Analysis for Finance, Payroll,
• Developed Fully Automated Business Model driven by KPI