Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Duration 35 hours
Course Outline
AI Fundamentals and Python with AI for Finance
AI, Analytics and Agentic AI in Modern Finance
- Distinctions between generative AI, machine learning, automation, and agentic AI, and their respective roles in finance.
- Finance applications across accounting, FP&A, reporting, audit, treasury, and shared services.
- Distinguishing tasks suitable for AI assistance versus those requiring controlled automation.
Python for Finance - Using AI as a Coding Partner
- Essential Python concepts for finance professionals: variables, data types, conditions, functions, and notebooks.
- Utilizing AI assistants to generate, explain, debug, and refine Python code, moving beyond isolated coding.
- Prompting techniques to ensure reliable, finance-focused code generation.
Working with Financial Data in Python
- Importing Excel and CSV data using Pandas and DataFrames.
- Filtering, grouping, aggregating, and calculating key finance metrics.
- Leveraging AI to explain errors, enhance logic, and document analysis steps.
Practical Finance Coding Applications
- Automating repetitive calculations, variance analysis, and ratio analysis.
- Developing reusable Python workflows supported by AI-based code reviews.
- Validating outputs prior to their use in finance reporting.
Hands-on Application
- Construct an AI-assisted Python workflow to analyse a sample finance dataset.
- Review generated code, test assumptions, and refine outputs through human validation.
Advanced Financial Data Analysis with AI
Financial Data Preparation and Quality
- Cleaning, validating, and standardizing finance data.
- Addressing missing values, duplicates, inconsistent classifications, and date discrepancies.
- Integrating data from multiple finance sources for comprehensive analysis.
Advanced Financial Analysis
- Analysis of revenue, cost, margin, profitability, and working capital.
- Comparison of budget versus actual, variance analysis, and period-over-period trends.
- Drill-down analysis to pinpoint key financial drivers.
AI-Assisted Analysis and Anomaly Detection
- Employing AI to investigate movements, patterns, and unusual transactions.
- Generating analytical questions and hypotheses from finance data.
- Distinguishing valuable signals from misleading AI-generated interpretations.
Forecasting and Scenario Analysis
- Historical trends, drivers, and assumptions informing forecasts.
- What-if and sensitivity analysis for finance decision support.
- Using AI to support scenario narratives while maintaining financial controls.
Hands-on Application
- Conduct end-to-end analysis of a finance dataset to identify key variances and anomalies.
- Draft a concise, AI-assisted finance insight summary backed by underlying data.
AI-Based Financial Dashboards and Management Insights
Finance Dashboard Design
- Selecting meaningful KPIs for finance, management, and operational reporting.
- Designing dashboards centered on decision-making questions rather than visual volume.
- Structuring views for executive, management, and analyst audiences.
Building Interactive Financial Dashboards
- Connecting and transforming finance data for dashboard utilization.
- Creating KPI cards, trends, variance visuals, drill-downs, and filters.
- Developing views for budget versus actual, profitability, cash flow, and performance.
AI-Enhanced Dashboarding
- Exploring financial data through natural-language querying.
- Generating AI-assisted summaries and explanations of KPI fluctuations.
- Identifying areas requiring deeper analysis with AI support.
Dashboard Controls and Reliability
- Considerations for data refresh, traceability, validation, and reconciliation.
- Managing access, sensitive financial information, and controlled distribution.
- Preventing misleading visual or AI-generated conclusions.
Hands-on Application
- Construct an interactive financial dashboard using a structured dataset.
- Incorporate AI-supported management commentary linked to measurable financial movements.
Advanced AI Tools in General Ledger and Finance Operations
AI Applications in General Ledger
- Analyzing GL accounts, transaction patterns, and posting behaviors.
- Supporting transaction classification and account-level reviews with AI.
- Detecting unusual, high-risk, or out-of-pattern entries.
AI for Reconciliations
- Matching records and identifying exceptions across finance datasets.
- Supporting bank, intercompany, and balance-sheet reconciliations.
- Prioritizing unreconciled items for human investigation.
Journal Entry Analytics
- Detection of duplicate, unusual, and manual journal entries.
- Period-end journal analysis and the generation of supporting explanations.
- Risk indicators and review checkpoints for finance teams.
AI in Financial Close and Reporting
- Close task prioritization and exception-based review.
- AI-assisted variance explanations, commentary, and review notes.
- Implementing structured approval and validation before final reporting.
Hands-on Application
- Analyze a sample GL dataset to identify anomalies and reconciliation exceptions.
- Prepare a controlled, AI-assisted review summary for finance management.
Agentic AI for Finance Operations and Decision Support
Understanding Agentic AI for Finance
- Components of agentic AI workflows: goals, planning, tools, memory, actions, and feedback loops.
- Identifying where agentic AI supports finance operations and where human approval is critical.
- Comparing single-agent versus multi-step or multi-agent finance workflows.
Designing Agentic Finance Workflows
- Creating agents for data collection, analysis, validation, and reporting tasks.
- Linking agents to structured finance data and approved tools.
- Defining escalation rules, checkpoints, and approval boundaries.
Agentic Use Cases in Finance
- Automated variance investigation and management commentary workflows.
- GL exception triage, reconciliation support, and close-status monitoring.
- Forecast refresh, scenario preparation, and finance query assistants.
Governance, Risk and Controls for Agentic AI
- Human-in-the-loop controls, audit trails, permissions, and segregation of duties.
- Data confidentiality, hallucination risks, validation, and model limitations.
- Establishing safe operating boundaries before production deployment.
Final Practical Capstone
- Integrate Python with AI, advanced analytics, and dashboard outputs in a single finance use case.
- Design an agentic workflow that analyses results, flags exceptions, and prepares management insights.
- Present the workflow, controls, outputs, and recommended next steps
Requirements
- A fundamental grasp of finance, accounting, financial reporting, or FP&A concepts.
- Proficiency with Excel and the ability to work with financial datasets.
- No prior Python programming experience is necessary, though basic exposure to data analysis is advantageous.
- Basic familiarity with AI or generative AI tools like ChatGPT, Microsoft Copilot, or Claude is helpful but not mandatory.
- Comfort working with financial reports, KPIs, budgets, variances, and related finance data.
- A laptop equipped with access to the required training tools, datasets, and approved AI platforms for hands-on sessions.
Testimonials (2)
the tips and recommended prompts that we can take away from this training
Lee Mei Lin - ST Engineering IHQ Pte Ltd
Course - InVideo AI: Creating Engaging Short-Form Videos
The content, the exchanges, and working on topics that were relevant to our daily lives
Sally - TEVA Sante
Course - Advanced AI Concepts with Perplexity
Machine Translated
