Introduction
The programme “Artificial Intelligence in Accounting: From Automation to Decision-Making” develops professional practice in Accounting. It connects the subject with workplace challenges by examining existing evidence and practices and choosing methods appropriate to the organization and its available resources.
The learning progresses from the accounting cycle, closing, and record quality to cash, liquidity, and working capital management, with practical work on artificial intelligence applications and controls, designing digital transformation and developing services, financial statement and report analysis. Participants prepare outputs to support workplace application and review the quality and results of implementation.
Learning objectives
- Process an accounting cycle and prepare adjustments supported by traceable documents in an application related to “Artificial Intelligence in Accounting: From Automation to Decision-Making”.
- Design an AI use case with verification criteria and human review.
- Assess digitization opportunities and define implementation requirements that improve a service or process.
- Interpret performance, financial position, and cash flows using documented analysis with clear limitations.
- Prepare a cash forecast and options for addressing liquidity and working capital gaps.
Learning areas
1. The Accounting Cycle, Closing, and Record Quality
- Trace a transaction from source document to journal entry, general ledger, and trial balance, identifying control points and approval responsibilities.
- Analyze transactions and classify accounts by their nature, checking completeness, cutoff, and correct accounting treatment.
- Prepare accounting adjustments and supporting reconciliations, documenting differences, their causes, and the parties responsible for resolving them.
- Review recognition, measurement, presentation, and disclosure requirements under the applicable accounting framework without assuming uniform requirements across countries.
- Organize the closing file with supporting documents, a timetable, and approval reviews, and prepare a register of matters requiring professional judgment. The application examines a case related to “Artificial Intelligence in Accounting: From Automation to Decision-Making”.
2. Artificial Intelligence Applications and Controls
- Identify a task that AI can support, and separate inputs and human decisions from outputs produced by the tool.
- Prepare suitable test data and examples while respecting confidentiality, privacy, and permissions, and use de-identified training exercise data.
- Write clear instructions covering the task, context, constraints, and output format, and compare multiple formulations using a fixed set of examples.
- Check outputs for errors, bias, and unsupported information, and identify cases that require specialist review.
- Assess the feasibility of use in terms of quality, time, cost, and risk, and document controls for approval, monitoring, and stopping when failures occur.
3. Designing Digital Transformation and Developing Services
- Distinguish information digitization, step automation, and business model transformation, and identify the problem targeted by the digital intervention.
- Map the beneficiary journey, touchpoints, and repeated data requests, and identify opportunities for simplification before selecting technology.
- Analyze readiness across procedures, skills, data, and technical infrastructure, and document gaps and dependencies on existing systems.
- Specify solution, integration, access permission, usability, and accessibility requirements, and establish testable acceptance criteria.
- Design a limited-scope pilot with indicators for time, quality, and use, and define conditions for continuing, adjusting, or stopping.
4. Financial Statement and Report Analysis
- Read the relationships between the statements of financial position, performance, and cash flows and the notes, identifying the effects of accounting policies and estimates.
- Perform horizontal and vertical analysis and compare profitability, liquidity, efficiency, and leverage indicators, considering the nature and size of the business.
- Examine earnings quality, cash flows, and nonrecurring items, and identify differences between accounting results and actual liquidity.
- Test the sensitivity of results to changes in sales, costs, collections, and financing, stating the assumptions and constraints in each scenario.
- Write a financial report linking indicators to management's question and available alternatives, presenting risks and additional data needed for the decision.
5. Cash, Liquidity, and Working Capital Management
- Analyze the cash cycle from purchasing to collection, identifying the effects of inventory, receivables, and payment terms on financing needs.
- Forecast receipts and payments by maturity, and document data sources and the likelihood of delays in expected cash flows.
- Classify receivables and debt aging, track collections and settlements, and connect follow-up actions to approved authorities and policies.
- Compare options for addressing deficits or deploying surpluses according to cost, liquidity, risk, and contractual constraints requiring review.
- Test a cash stress scenario, set alert thresholds and escalation actions, and update forecasts based on actual results.
Practical outputs
- An accounting closing file, adjustments, and a record quality checklist.
- A use case brief and a test template for the quality of AI outputs.
- A digital transformation roadmap covering the beneficiary journey and implementation requirements.
- A comparative financial analysis report and a memo of findings and assumptions.
- A cash flow forecast and a plan to improve collections and the cash conversion cycle.
Who should attend
- Practitioners, specialists and supervisors whose work relates to the programme subject.
- Managers and teams working in accounting, according to their practical responsibilities.
- Professionals responsible for applying the outputs of “Artificial Intelligence in Accounting: From Automation to Decision-Making” within their organizations.
Prerequisites
- Working familiarity with the programme field and the ability to discuss a professional case or challenge.
- Confirm the suitability of your experience and any specialist prerequisites with the Union before participation.
Conclusion
The programme “Artificial Intelligence in Accounting: From Automation to Decision-Making” brings together concepts, case review and a practical application. Participants prepare steps that can be adapted to their responsibilities and organizational resources, with an indicator to follow application and review results after returning to work.
Participation arrangements and any special requests are specified in the confirmation issued by the Union.