7 Topics
5 Days
35 Sessions
Programme Overview
Training Description
- Management accountants
- Financial controllers
- Treasury professionals
- Corporate planning professionals
- Business and data analysts
- Finance transformation professionals
- Strategy and performance-management professionals
- Accounting and reporting professionals
- Risk and compliance professionals
- Senior executives responsible for financial planning and decision-making
Session Objectives
- Grasp the fundamental concepts of AI and machine learning in a financial context. Identify opportunities to apply AI to forecasting and budgeting processes. Learn how to prepare and clean financial data for machine learning models. Understand and use key predictive algorithms for financial forecasting. Interpret AI-generated insights to make better strategic decisions.
About the Course
Intelligent Budgeting and Forecasting: AI, Analytics and Financial Strategy is an advanced professional training course designed to help finance and business professionals transform traditional budgeting and forecasting through artificial intelligence, predictive analytics, automation, and data-driven financial strategy.The course examines how AI and advanced analytics can improve forecast accuracy, identify financial trends, predict budget variances, optimize resource allocation, automate reporting, and support strategic decision-making. Participants will also explore AI governance, data quality, model risks, cybersecurity, and the importance of human judgment when applying intelligent technologies to financial planning.
Curriculum & Topics
7 Topics | 35 Sessions
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Workshop 1.1:
What is AI, machine learning, and deep learning?
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Workshop 1.2:
The evolution of financial forecasting from spreadsheets to predictive models.
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Workshop 1.3:
Identifying common use cases for AI in budgeting and analysis.
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Workshop 1.4:
Understanding the ecosystem of AI tools and platforms.
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Workshop 1.5:
Introduction to data requirements for AI models.
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Workshop 2.1:
The importance of data quality, cleanliness, and integrity.
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Workshop 2.2:
Techniques for data cleansing, feature engineering, and normalization.
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Workshop 2.3:
Handling time-series data and seasonal trends.
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Workshop 2.4:
Integrating data from multiple sources (ERPs, CRMs, market data).
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Workshop 2.5:
Ensuring data security and compliance with financial regulations.
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Workshop 3.1:
Introduction to regression models for forecasting.
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Workshop 3.2:
Overview of machine learning models: decision trees and gradient boosting.
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Workshop 3.3:
Understanding neural networks for complex pattern recognition.
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Workshop 3.4:
Choosing the right model for specific business challenges.
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Workshop 3.5:
Working with time-series models like ARIMA and Prophet.
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Workshop 4.1:
Practical, hands-on session using a common AI tool.
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Workshop 4.2:
Splitting data for training, validation, and testing.
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Workshop 4.3:
Evaluating model performance using metrics like MAE, RMSE, and R-squared.
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Workshop 4.4:
Techniques for preventing overfitting and improving model accuracy.
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Workshop 4.5:
Interpreting model outputs and understanding feature importance.
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Workshop 5.1:
Translating AI-generated forecasts into a strategic budget.
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Workshop 5.2:
Conducting "what-if" scenario analysis with AI predictions.
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Workshop 5.3:
Automating budget variance analysis and anomaly detection.
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Workshop 5.4:
Using AI for dynamic resource allocation and performance monitoring.
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Workshop 5.5:
Creating compelling visualizations of AI insights for stakeholders.
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Workshop 6.1:
Developing a pilot project for AI in budgeting.
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Workshop 6.2:
Building a business case for AI investment.
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Workshop 6.3:
Navigating the ethical implications of AI, including bias and transparency.
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Workshop 6.4:
Establishing governance and oversight for AI models.
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Workshop 6.5:
Training teams and managing change in the finance department.
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Workshop 7.1:
Emerging trends in AI, such as Generative AI and Large Language Models.
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Workshop 7.2:
Integrating AI with other technologies like blockchain and IoT.
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Workshop 7.3:
The evolving role of the finance professional in an AI-driven world.
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Workshop 7.4:
Creating a roadmap for continuous innovation.
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Workshop 7.5:
Open Q&A and a summary of key takeaways.
