Advanced Data Analytics, Dashboards & Agentic Intelligence Finance Operations Training Course

Day 1: AI Fundamentals and AI-Driven Python for Finance

AI, Analytics, and Agentic AI in Contemporary Finance

• Differentiating generative AI, machine learning, automation, and agentic AI, and understanding their respective roles in finance.
• Exploring finance use cases across accounting, FP&A, reporting, audit, treasury, and shared services.
• Identifying tasks 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, conditionals, functions, and notebooks.
• Utilising AI assistants to generate, explain, debug, and refine Python code, rather than coding in isolation.
• Prompting techniques for generating reliable, finance-focused code.

Handling Financial Data in Python

• Importing Excel and CSV data using Pandas and DataFrames.
• Filtering, grouping, aggregating, and calculating key finance metrics.
• Using AI to elucidate errors, optimise logic, and document analysis steps.

Practical Finance Coding Applications

• Automating repetitive calculations, variance analysis, and ratio analysis.
• Creating reusable Python workflows with AI-supported code reviews.
• Validating outputs prior to their use in finance reporting.

Hands-on Exercise

• Construct an AI-assisted Python workflow to analyse a sample finance dataset.
• Review generated code, test assumptions, and refine outputs through human validation.

Day 2: Advanced Financial Data Analysis with AI

Financial Data Preparation and Quality Assurance

• Cleaning, validating, and standardising finance data.
• Addressing missing values, duplicates, inconsistent classifications, and date discrepancies.
• Integrating data from multiple finance sources for comprehensive analysis.

Advanced Financial Analysis Techniques

• Analysing revenue, costs, margins, profitability, and working capital.
• Conducting budget versus actual, variance, and period-over-period analysis.
• Performing 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

• Examining historical trends, drivers, and assumptions for forecasting.
• Conducting what-if and sensitivity analysis to support finance decisions.
• Using AI to support scenario narratives while maintaining financial controls.

Hands-on Exercise

• Execute end-to-end analysis of a finance dataset to identify key variances and anomalies.
• Prepare a concise, AI-assisted finance insight summary backed by underlying data.

Day 3: AI-Driven Financial Dashboards and Management Insights

Finance Dashboard Design Principles

• Selecting meaningful KPIs for finance, management, and operational reporting.
• Designing dashboards centred on decision questions rather than visual density.
• Structuring views for executive, management, and analyst levels.

Constructing Interactive Financial Dashboards

• Connecting and transforming finance data for dashboard integration.
• Creating KPI cards, trends, variance visuals, drill-downs, and filters.
• Building views for budget versus actual, profitability, cash flow, and performance.

AI-Enhanced Dashboarding

• Utilising natural-language querying to explore financial data.
• Generating AI-assisted summaries and explanations of KPI movements.
• Leveraging AI to identify areas requiring deeper analysis.

Dashboard Controls and Reliability

• Considering data refresh, traceability, validation, and reconciliation.
• Managing access, sensitive financial information, and controlled distribution.
• Avoiding misleading visual or AI-generated conclusions.

Hands-on Exercise

• Build an interactive financial dashboard using a structured dataset.
• Incorporate AI-supported management commentary linked to measurable financial movements.

Day 4: Advanced AI Tools for General Ledger and Finance Operations

AI Applications in General Ledger Management

• Analysing GL accounts, transaction patterns, and posting behaviour.
• Using AI to support transaction classification and account-level reviews.
• Identifying 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.
• Prioritising unreconciled items for human investigation.

Journal Entry Analytics

• Detecting duplicate, unusual, and manual journal entries.
• Analysing period-end journals and generating supporting explanations.
• Establishing risk indicators and review checkpoints for finance teams.

AI in Financial Close and Reporting

• Prioritising close tasks and conducting exception-based reviews.
• Generating AI-assisted variance explanations, commentary, and review notes.
• Implementing structured approval and validation before final reporting.

Hands-on Exercise

• Analyse a sample GL dataset to identify anomalies and reconciliation exceptions.
• Produce a controlled, AI-assisted review summary for finance management.

Day 5: 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.
• Determining where agentic AI can support finance operations and where human approval is critical.
• Distinguishing between single-agent and multi-step or multi-agent finance workflows.

Designing Agentic Finance Workflows

• Creating agents for data collection, analysis, validation, and reporting tasks.
• Connecting agents to structured finance data and approved tools.
• Designing escalation rules, checkpoints, and approval boundaries.

Agentic Use Cases in Finance

• Implementing automated variance investigation and management commentary workflows.
• Utilising GL exception triage, reconciliation support, and close-status monitoring.
• Facilitating forecast refresh, scenario preparation, and finance query assistance.

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 prior to production deployment.

Final Practical Capstone

• Integrate Python with AI, advanced analytics, and dashboard outputs into 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