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
- Distinguishing between generative AI, machine learning, automation, and agentic AI, and identifying the appropriate application for each within finance.
- Exploring financial use cases across accounting, FP&A, reporting, audit, treasury, and shared services.
- Determining which tasks are suitable for AI assistance versus those requiring controlled automation.
Python for Finance - Leveraging AI as a Coding Partner
- Essential Python concepts for finance professionals: variables, data types, conditional logic, functions, and notebooks.
- Utilizing AI assistants to generate, explain, debug, and refine Python code, fostering a collaborative coding approach rather than isolated development.
- Employing effective prompting techniques to ensure reliable, finance-specific code generation.
Managing Financial Data with Python
- Importing Excel and CSV data using Pandas and DataFrames.
- Filtering, grouping, aggregating, and calculating key financial metrics.
- Using AI to clarify errors, optimize logic, and document analytical steps.
Practical Applications of Finance Coding
- Automating repetitive calculations, variance analysis, and ratio computations.
- Developing reusable Python workflows enhanced by AI-supported code reviews.
- Validating outputs to ensure accuracy before integrating them into financial reporting.
Practical Exercise
- Create an AI-assisted Python workflow to analyze a sample financial dataset.
- Review the generated code, test underlying assumptions, and refine the output through human validation.
Advanced Financial Data Analysis with AI
Preparing and Ensuring Quality of Financial Data
- Cleaning, validating, and standardizing financial data.
- Addressing missing values, duplicates, inconsistent classifications, and date discrepancies.
- Integrating data from various financial sources for comprehensive analysis.
Deep-Dive Financial Analysis
- Analyzing revenue, costs, margins, profitability, and working capital.
- Conducting budget-versus-actual, variance, and period-over-period analyses.
- Performing drill-down analyses to pinpoint key financial drivers.
AI-Assisted Analysis and Anomaly Detection
- Utilizing AI to investigate financial movements, patterns, and irregular transactions.
- Generating analytical questions and hypotheses based on financial data.
- Differentiating between valuable insights and misleading AI-generated interpretations.
Forecasting and Scenario Planning
- Establishing historical trends, key drivers, and assumptions for forecasting.
- Conducting what-if and sensitivity analyses to support financial decisions.
- Using AI to craft scenario narratives while maintaining financial controls.
Practical Exercise
- Execute an end-to-end analysis of a financial dataset to identify significant variances and anomalies.
- Prepare a concise, AI-assisted summary of financial insights, backed by underlying data.
AI-Based Financial Dashboards and Management Insights
Designing Finance Dashboards
- Selecting relevant KPIs for financial, management, and operational reporting.
- Designing dashboards centered on decision-making questions rather than excessive visual complexity.
- Structuring views for executives, management, and analysts.
Creating Interactive Financial Dashboards
- Connecting and transforming financial data for dashboard integration.
- Developing KPI cards, trend visualizations, variance charts, drill-downs, and filters.
- Building views for budget versus actual, profitability, cash flow, and performance tracking.
Enhancing Dashboards with AI
- Using natural language queries to explore financial data.
- Generating AI-assisted summaries and explanations for KPI fluctuations.
- Leveraging AI to highlight areas requiring deeper analysis.
Dashboard Controls and Reliability
- Considering data refresh rates, traceability, validation, and reconciliation.
- Managing access controls, sensitive financial information, and distribution protocols.
- Preventing misleading visualizations or AI-generated conclusions.
Practical Exercise
- Construct an interactive financial dashboard using a structured dataset.
- Append AI-supported management commentary linked to specific financial movements.
Advanced AI Tools in General Ledger and Finance Operations
AI Applications in the General Ledger
- Analyzing GL accounts, transaction patterns, and posting behaviors.
- Using AI to aid in transaction classification and account-level reviews.
- Detecting unusual, high-risk, or out-of-pattern entries.
AI for Reconciliations
- Matching records and identifying exceptions across various financial datasets.
- Supporting bank, intercompany, and balance-sheet reconciliations.
- Prioritizing unreconciled items for human review.
Journal Entry Analytics
- Detecting duplicate, unusual, or manual journal entries.
- Analyzing period-end journals and generating supporting explanations.
- Identifying risk indicators and review checkpoints for finance teams.
AI in Financial Close and Reporting
- Prioritizing close tasks and conducting exception-based reviews.
- Generating AI-assisted variance explanations, commentary, and review notes.
- Implementing structured approval and validation processes before final reporting.
Practical Exercise
- Analyze a sample GL dataset to identify anomalies and reconciliation exceptions.
- Create a controlled, AI-assisted review summary for finance management.
Agentic AI for Finance Operations and Decision Support
Understanding Agentic AI in Finance
- Defining agentic AI workflows: goals, planning, tools, memory, actions, and feedback loops.
- Identifying where agentic AI can support finance operations and where human approval is critical.
- Distinguishing between single-agent and multi-step or multi-agent financial workflows.
Designing Agentic Finance Workflows
- Creating agents for data collection, analysis, validation, and reporting tasks.
- Connecting agents to structured financial data and approved tools.
- Establishing escalation rules, checkpoints, and approval boundaries.
Agentic Use Cases in Finance
- Automating variance investigations and generating management commentary.
- Triage GL exceptions, support reconciliations, and monitor close status.
- Refreshing forecasts, preparing scenarios, and assisting with finance queries.
Governance, Risk, and Controls for Agentic AI
- Implementing human-in-the-loop controls, audit trails, permissions, and segregation of duties.
- Managing data confidentiality, hallucination risks, validation, and model limitations.
- Defining safe operating boundaries before production deployment.
Final Practical Capstone
- Integrate Python with AI, advanced analytics, and dashboard outputs into a single financial use case.
- Design an agentic workflow that analyzes results, flags exceptions, and prepares management insights.
- Present the workflow, controls, outputs, and recommended next steps.
Requirements
- Fundamental knowledge of finance, accounting, financial reporting, or FP&A principles.
- Proficiency with Excel and experience manipulating financial datasets.
- No prior Python programming background is mandatory, though a basic familiarity with data analysis is advantageous.
- General awareness of AI or generative AI tools such as ChatGPT, Microsoft Copilot, or Claude is beneficial but not a strict requirement.
- Participants should feel confident working with financial reports, KPIs, budgets, variances, and associated financial data.
- A laptop equipped with access to the necessary training tools, datasets, and approved AI platforms is required for the practical sessions.
Testimonials (1)
the tips and recommended prompts that we can take away from this training
