Format: Live instructor-led online training via Zoom / Microsoft Teams

AI-DRIVEN FINANCIAL DECISION MAKING TRAINING COURSE

Course Introduction

AI-Driven Financial Decision Making Training Course is a comprehensive and practical professional programme designed to equip finance professionals, accountants, financial analysts, CFOs, treasury specialists, investment professionals, risk managers, auditors, and business leaders with advanced capabilities in Artificial Intelligence (AI), machine learning, predictive analytics, generative AI, financial modelling, data analytics, and intelligent financial decision-making. The programme explores how AI-powered financial analytics can transform corporate finance, financial planning and analysis (FP&A), budgeting, forecasting, investment decisions, treasury management, working capital management, risk assessment, financial performance management, and strategic decision-making. Participants will learn how to convert complex financial and business data into reliable, timely, and actionable financial insights.
The programme provides advanced coverage of AI-assisted financial forecasting, predictive financial modelling, scenario analysis, sensitivity analysis, variance analysis, profitability analysis, cash flow forecasting, liquidity management, investment analysis, credit risk assessment, and financial performance optimization. Participants will learn how to combine accounting information, ERP data, banking information, market intelligence, operational metrics, and economic indicators to develop AI-supported financial decisions. The training also covers generative AI for financial research, financial statement analysis, management reporting, executive summaries, financial modelling assistance, and decision-support workflows while emphasizing validation, professional judgement, and human oversight.
Participants will develop practical skills in identifying financial decision-making problems suitable for AI, preparing and evaluating financial datasets, interpreting predictive models, assessing AI-generated insights, developing AI-assisted financial scenarios, and using analytics to improve decision quality. The programme addresses AI governance, data quality, data privacy, cybersecurity, model risk, algorithmic bias, explainability, responsible AI, and human-in-the-loop decision-making. Relevant general case studies will demonstrate how organizations can apply AI to investment selection, liquidity planning, credit risk, working capital, budgeting, financial forecasting, fraud detection, profitability analysis, and strategic financial planning.
By the end of the training, participants will be able to use AI-driven financial analytics and decision-support technologies to evaluate alternatives, identify financial risks, forecast outcomes, optimize resources, and support strategic corporate decisions. The programme is suitable for corporations, banks, investment firms, government institutions, NGOs, development organizations, manufacturing companies, technology companies, and other organizations seeking data-driven financial management. The course can be customized according to the organization's ERP systems, financial data environment, analytical maturity, AI strategy, industry requirements, and strategic decision-making priorities.

Course Objectives

1. Understand the fundamentals of AI-driven financial decision-making and intelligent finance technologies.
2. Identify high-value AI applications across corporate finance, investment, treasury, accounting, and financial management.
3. Apply predictive analytics and machine learning to financial forecasting and decision analysis.
4. Use AI to improve budgeting, scenario planning, sensitivity analysis, and financial modelling.
5. Apply AI-driven analytics to cash flow, liquidity, working capital, and treasury decisions.
6. Evaluate investment opportunities using AI-powered financial and market analytics.
7. Apply AI to financial risk management, fraud detection, credit analysis, and anomaly detection.
8. Use generative AI for financial research, financial reporting, management analysis, and decision support.
9. Evaluate AI model outputs, data quality, model risk, bias, explainability, cybersecurity, and governance requirements.
10. Develop an AI-driven financial decision-making framework and transformation roadmap aligned with organizational strategy.

Organization Benefits

1. Improves the speed and quality of financial decision-making.
2. Strengthens financial forecasting and predictive analysis.
3. Enables data-driven investment and resource allocation decisions.
4. Improves budgeting, scenario planning, and financial modelling.
5. Strengthens liquidity, cash flow, and working capital management.
6. Enables early identification of financial risks and emerging business trends.
7. Improves fraud detection, anomaly identification, and financial control monitoring.
8. Enhances financial reporting, management information, and executive decision support.
9. Reduces manual financial analysis and improves finance team productivity.
10. Supports digital finance transformation and development of an intelligent, analytics-driven finance function.

Target Participants

The course is designed for Chief Financial Officers (CFOs), Finance Directors, Finance Managers, Financial Controllers, Accountants, Management Accountants, FP&A Professionals, Financial Analysts, Treasury Managers, Investment Analysts, Risk Managers, Auditors, Internal Auditors, Corporate Finance Professionals, Business Analysts, Data Analysts, ERP Professionals, Strategy Managers, and senior executives responsible for finance, investment, risk management, financial planning, reporting, and digital transformation.

Course Outline

Module 1: Foundations of AI-Driven Financial Decision Making

1. Introduction to Artificial Intelligence, machine learning, predictive analytics, generative AI, and intelligent decision systems.
2. Evolution of traditional financial decision-making toward data-driven and AI-enabled financial management.
3. AI applications in financial planning, investment, treasury, risk management, accounting, and corporate strategy.
4. Financial data sources including ERP systems, accounting systems, banking data, market data, operational data, and economic indicators.
5. Identifying AI use cases based on financial value, data readiness, feasibility, risk, and decision impact.
6. General Case Study: Developing an AI decision-making opportunity map for a finance department covering forecasting, investment, liquidity, risk, budgeting, and financial performance.

Module 2: AI-Powered Financial Forecasting and Scenario Analysis

1. AI-powered financial forecasting, predictive modelling, trend analysis, and financial projections.
2. Machine learning applications in revenue, expenditure, profitability, cash flow, and working capital forecasting.
3. AI-assisted budgeting, rolling forecasts, driver-based planning, and dynamic financial models.
4. Scenario analysis, sensitivity analysis, stress testing, what-if analysis, and predictive business modelling.
5. AI-supported variance analysis, budget-versus-actual analysis, performance drivers, and management interpretation.
6. General Case Study: Developing an AI-supported financial forecasting model to evaluate revenue, expenses, profitability, cash flow, and multiple business scenarios.

Module 3: AI for Investment and Capital Allocation Decisions

1. AI applications in investment analysis, financial modelling, valuation, market intelligence, and opportunity assessment.
2. Predictive analytics for investment performance, market trends, financial indicators, and investment risk.
3. AI-supported company analysis, competitor analysis, industry intelligence, and financial statement evaluation.
4. Capital allocation, investment prioritization, portfolio analysis, and risk-adjusted decision-making.
5. Scenario modelling and sensitivity analysis for investment appraisal and strategic capital expenditure decisions.
6. General Case Study: Using AI-driven financial analytics to evaluate alternative investment opportunities based on expected returns, risk, cash flows, market conditions, and strategic alignment.

Module 4: AI for Treasury, Cash Flow and Risk Decisions

1. AI-powered cash flow forecasting, liquidity planning, cash positioning, and treasury decision-making.
2. Working capital analytics covering receivables, payables, inventory, cash conversion cycles, and liquidity optimization.
3. AI-driven credit risk assessment, customer payment behaviour, counterparty risk, and financial exposure analysis.
4. Foreign exchange, interest rate, liquidity risk, and financial market analytics using predictive technologies.
5. AI-powered anomaly detection, fraud analytics, unusual transaction identification, and financial risk monitoring.
6. General Case Study: Developing an AI-driven treasury decision-support model for optimizing liquidity, working capital, foreign exchange exposure, credit risk, and cash utilization.

Module 5: Generative AI, Financial Analytics and Executive Decision Support

1. Generative AI applications in financial research, financial statement analysis, reporting, and management decision support.
2. AI-assisted interpretation of financial statements, management accounts, budgets, forecasts, and performance reports.
3. Automated financial narratives, executive summaries, management commentary, and financial insights.
4. Prompt engineering for finance professionals, structured financial queries, output validation, and human review.
5. AI-powered dashboards, business intelligence, financial visualization, and executive decision-support systems.
6. General Case Study: Designing a generative AI workflow that analyses monthly financial results, identifies key performance drivers, explains significant variances, and produces an executive decision brief.

Module 6: AI Governance, Responsible Decision Making and Finance Transformation

1. AI governance, responsible AI, model risk management, transparency, explainability, and human oversight.
2. Financial data quality, privacy, cybersecurity, access controls, data governance, and regulatory considerations.
3. Algorithmic bias, model limitations, hallucinations, reliability assessment, validation, and professional judgement.
4. Evaluating AI solutions based on accuracy, business value, implementation cost, scalability, risk, and return on investment.
5. Developing AI adoption strategies, finance transformation roadmaps, performance indicators, and continuous improvement frameworks.
6. General Case Study: Developing an organization-wide AI-driven financial decision-making framework integrating people, processes, data, technology, governance, cybersecurity, financial analytics, and strategic decision-making.

General Information

1. Customized Training: All our courses can be tailored to meet the specific needs of participants.
2. Language Proficiency: Participants should have a good command of the English language.
3. Comprehensive Learning: Our training includes well-structured presentations, practical exercises, web-based tutorials, and collaborative group work. Our facilitators are seasoned experts with over a decade of experience.
4. Certification: Upon successful completion of training, participants will receive a certificate from Foscore Development Center (FDC-K).
5. Training Locations: Training sessions are conducted at Foscore Development Center (FDC-K) centers. We also offer options for in-house and online training, customized to the client's schedule.
6. Flexible Duration: Course durations are adaptable, and content can be adjusted to fit the required number of days.
7. Onsite Training Inclusions: The course fee for onsite training covers facilitation, training materials, two coffee breaks, a buffet lunch, and a Certificate of Successful Completion. Participants are responsible for their travel expenses, airport transfers, visa applications, dinners, health/accident insurance, and personal expenses.
8. Additional Services: Accommodation, pickup services, flight booking, and visa processing arrangements are available upon request at discounted rates.
9. Equipment: Tablets and laptops can be provided to participants at an additional cost.
10. Post-Training Support: We offer one year of free consultation and coaching after the course.
11. Group Discounts: Register as a group of more than two and enjoy a discount ranging from 10% to 50%.
12. Payment Terms: Payment should be made before the commencement of the training or as mutually agreed upon, to the Foscore Development Center account. This ensures better preparation for your training.
13. Contact Us: For any inquiries, please reach out to us at training@fdc-k.org or call us at +254712260031.
14. Website: Visit our website at www.fdc-k.org for more information.

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