Role-Based Programme
RB1391

AI for Financial Reporting & Controllership

Advanced Financial Close, Reporting Intelligence & Control Excellence

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Duration
32 Hours
Level
Advanced
Delivery
Instructor-Led
Format
Capability Development

Programme Objectives

  • Develop advanced capability to apply AI across financial close, accounting review, reconciliations, financial reporting, consolidation, and controllership.
  • Use AI-assisted analysis to identify reporting anomalies, reconciliation breaks, unusual journal activity, control gaps, close bottlenecks, and financial-performance movements.
  • Apply AI to account analysis, variance investigation, financial statement reviews, close management, audit preparation, controls monitoring, and management reporting.
  • Use AI-assisted analytics to improve reporting accuracy, close efficiency, financial transparency, control effectiveness, audit readiness, and management visibility.
  • Build responsible AI-enabled Financial Reporting and Controllership workflows with strong financial governance, confidentiality, validation, traceability, segregation of duties, and human oversight.

Tools covered

Generative AI AssistantsAI Search & ResearchFinancial Reporting AnalyticsFinancial Close IntelligenceAccount Reconciliation AnalysisJournal Entry AnalysisVariance AnalysisConsolidation SupportFinancial Statement AnalysisControl MonitoringAudit SupportManagement ReportingWorkflow AutomationAI AgentsDecision-Support Tools

Who should attend

  • Financial Controllers
  • Corporate Controllers
  • Financial Reporting Managers
  • Accounting Managers
  • General Ledger Managers
  • Record-to-Report Managers
  • Financial Reporting Analysts
  • Senior Accountants
  • General Ledger Accountants
  • Consolidation Professionals
  • Finance Control Professionals
  • Management Reporting Professionals
  • Finance Shared Services Professionals
  • Finance Transformation Professionals
  • Leaders Responsible for Financial Reporting & Controllership

Prerequisites & Participant Readiness

  • Experience in accounting, financial reporting, controllership, general ledger, record-to-report, or finance operations
  • Familiarity with financial statements, journal entries, reconciliations, month-end close, accounting controls, and management reporting
  • Basic awareness of Generative AI and common business applications
  • Comfort working with financial data, accounting schedules, reports, and reconciliations
  • No programming knowledge required

TOC Modules

Concepts
  • Understanding Generative AI, reasoning models, analytics, document intelligence, automation, and AI agents
  • Exploring AI applications across accounting, financial close, reporting, reconciliations, controls, and audit support
  • Distinguishing AI-assisted accounting analysis from authorised accounting judgments and approvals
  • Understanding hallucinations, financial-data sensitivity, control risk, and human accountability
Practical activities
  • Mapping AI opportunities across the Financial Reporting and Controllership lifecycle
  • Identifying activities suitable for augmentation, automation, or continued human ownership
  • Creating an AI opportunity map for Controllership teams

Scenarios

Delayed Month-End Close to AI-Enabled Close Acceleration Plan

Close Calendar → Account Status → Journal Entries → Reconciliations → Intercompany Breaks → Bottlenecks → Control Review → Close Dashboard

Participants use AI to analyse a consistently delayed financial close, identify recurring reconciliation, journal, intercompany, and workflow issues, and create a controlled close-acceleration plan without compromising financial accuracy.

Financial Reporting Anomalies to Controllership Assurance Model

Financial Statements → Account Movements → Journal Exceptions → Reconciliation Breaks → Control Gaps → Audit Evidence → Remediation → Executive Reporting

Participants use AI to investigate unusual financial-reporting movements, trace potential causes across accounting records and controls, prioritise remediation, and build a stronger controllership monitoring and assurance framework.

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