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

AI Fundamentals and Python with AI for Finance

AI, Analytics, and Agentic AI in Modern Finance

• Understanding the distinctions between generative AI, machine learning, automation, and agentic AI, and where each fits within 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 – Using AI as a Coding Partner

• Essential Python concepts for finance professionals: variables, data types, conditions, functions, and notebooks.
• Utilising AI assistants to generate, explain, debug, and refine Python code, moving beyond isolated coding practices.
• Techniques for effective prompting 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.
• Employing AI to explain errors, refine 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 review.
• Validating outputs prior to their use in finance reporting.

Hands-on Application

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

Advanced Financial Analysis

• Analysis of revenue, costs, margins, profitability, and working capital.
• Budget versus actual, variance, and period-over-period analysis.
• Drill-down analysis to identify key financial drivers.

AI-Assisted Analysis and Anomaly Detection

• Using AI to investigate movements, patterns, and unusual transactions.
• Generating analytical questions and hypotheses from finance data.
• Distinguishing meaningful signals from misleading AI-generated interpretations.

Forecasting and Scenario Analysis

• Historical trends, drivers, and assumptions for forecasting.
• What-if and sensitivity analysis to support finance decisions.
• Leveraging 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.
• Prepare a concise, AI-assisted finance insight summary supported by underlying data.

AI-Based Financial Dashboards and Management Insights

Finance Dashboard Design

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

Building Interactive Financial Dashboards

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

AI-Enhanced Dashboarding

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

Dashboard Controls and Reliability

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

Hands-on Application

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

Advanced AI Tools in General Ledger and Finance Operations

AI Applications in General Ledger

• 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 journals.
• Period-end journal analysis and supporting explanation generation.
• Risk indicators and review checkpoints for finance teams.

AI in Financial Close and Reporting

• Close task prioritisation and exception-based review.
• AI-assisted variance explanations, commentary, and review notes.
• Implementing structured approval and validation prior to final reporting.

Hands-on Application

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

Agentic AI for Finance Operations and Decision Support

Understanding Agentic AI for 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 essential.
• 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.
• Connecting agents to structured finance data and approved tools.
• Designing 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

• Implementing human-in-the-loop controls, audit trails, permissions, and segregation of duties.
• Addressing data confidentiality, hallucination risk, validation, and model limitations.
• Defining safe operating boundaries prior to production deployment.

Final Practical Capstone

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