Treasury & Budgeting
Artificial Intelligence (AI) for Treasury & Financial Decision-Making
This five-day programme prepares finance professionals to use AI as a working tool rather than a headline. Participants build skills in financial data preparation, AI-based forecasting, anomaly detection and document intelligence, and model evaluation with responsible governance.
Introduction
Artificial Intelligence (AI) for Treasury & Financial Decision-Making Training Course prepares finance professionals to use AI as a working tool rather than a headline. Participants work through data foundations, forecasting with machine learning, cash flow prediction, anomaly detection, and decision support as one connected practice. The programme connects AI capability with treasury and finance problems that actually recur, such as liquidity forecasting and fraud detection. As a result, learners gain a practical method for applying AI where it earns its place. Furthermore, the course strengthens analytical judgement, model literacy, and the governance that keeps AI use responsible.
Artificial Intelligence (AI) for Treasury & Financial Decision-Making Course Objectives
- Explain what AI, machine learning, and generative AI actually mean in finance.
- Identify treasury and finance use cases where AI adds measurable value.
- Prepare financial data for analysis: cleaning, structuring, and feature selection.
- Apply time series forecasting methods for cash flow and revenue prediction.
- Use classification and anomaly detection models for fraud and error detection.
- Apply natural language processing (NLP) to financial documents and contracts.
- Use generative AI tools for analysis, drafting, and scenario summarisation.
- Evaluate model accuracy with error metrics such as MAE, RMSE, and MAPE.
- Recognise bias, hallucination, and model risk in financial AI outputs.
- Apply AI governance, data protection, and auditability controls.
Course Methodology
- Interactive sessions built on treasury and finance AI use cases.
- Workshops on data preparation, forecasting, and anomaly detection.
- Group exercises on prompt design, model evaluation, and use case selection.
- Hands-on practice with forecasting models, scoring methods, and generative AI tools.
- Simulation of a fraud detection case and an AI-assisted decision review.
- Facilitator coaching and peer review on personal AI adoption plans.
Who Should Take This Course
- Treasury managers, analysts, and cash managers.
- Finance managers, controllers, and financial analysts.
- Financial planning and analysis (FP&A) professionals.
- Risk, credit, and internal control professionals.
- Data analysts supporting finance functions.
- Consultants advising on finance digitalisation and automation.
Artificial Intelligence (AI) for Treasury & Financial Decision-Making Course Outlines
AI Foundations for Finance Professionals
- • AI, machine learning, deep learning, and generative AI compared.
- • Supervised, unsupervised, and reinforcement learning in simple terms.
- • How AI differs from traditional financial modelling.
- • Treasury and finance use cases that deliver measurable value.
- • Data as the foundation: availability, quality, and ownership.
- • Common myths, overpromises, and realistic expectations.
- • Case discussion: an AI project that solved a problem nobody had.
Financial Data Preparation and Feature Engineering
- • Sourcing financial data: ERP, banking, market, and internal systems.
- • Data cleaning: duplicates, gaps, outliers, and inconsistencies.
- • Structuring time series data for forecasting.
- • Feature engineering: lags, seasonality, trends, and external drivers.
- • Data quality checks and validation before modelling.
- • Data protection and confidentiality in financial datasets.
- • Workshop: preparing a cash flow dataset for modelling.
Forecasting and Predictive Analytics
- • Time series methods: moving average, exponential smoothing, and ARIMA basics.
- • Machine learning forecasting with regression and gradient boosting concepts.
- • Cash flow forecasting and working capital prediction.
- • Revenue, cost, and demand forecasting applications.
- • Forecast accuracy metrics: MAE, RMSE, MAPE, and bias tracking.
- • Scenario simulation and stress testing with predictive models.
- • Exercise: building and comparing two forecasting approaches.
Detection, Classification, and Document Intelligence
- • Anomaly detection for fraud, error, and unusual transactions.
- • Classification models for credit risk and payment behaviour scoring.
- • Clustering for customer and cost segmentation.
- • Natural language processing (NLP) for contracts and financial documents.
- • Generative AI for analysis, drafting, and summarisation.
- • Prompt design and verification of generative AI outputs.
- • Simulation: investigating a suspicious transaction pattern with AI support.
Model Risk, Governance, and Decision Integration
- • Model risk: bias, hallucination, drift, and overfitting.
- • Explainability and the need for human review in decisions.
- • Validation, backtesting, and model performance monitoring.
- • AI governance, data protection, and auditability requirements.
- • Integrating AI outputs into treasury and finance workflows.
- • Building an AI adoption roadmap and capability plan.
- • Capstone: presenting an AI use case for a finance function.
Conclusion
By successfully completing Artificial Intelligence (AI) for Treasury & Financial Decision-Making, participants will gain a practical command of data preparation, forecasting, anomaly detection, and generative AI use in finance. They will acquire skills in model evaluation, AI-assisted cash flow prediction, fraud detection support, and responsible AI governance. Gentex Training Center designs the programme to help finance professionals apply AI where it earns its place.
FAQs
— What is the "Artificial Intelligence (AI) for Treasury & Financial Decision-Making" course about?
It prepares finance professionals to use AI as a working tool rather than a headline. The course spans AI foundations for finance, financial data preparation and feature engineering, forecasting and predictive analytics, detection, classification and document intelligence, and model risk, governance, and decision integration.
— What are the key benefits of the "Artificial Intelligence (AI) for Treasury & Financial Decision-Making" course?
You gain a practical method for applying AI where it earns its place. The course improves data preparation discipline, strengthens cash flow and revenue forecasting, speeds up fraud and anomaly detection, and gives you governance controls that make AI use defensible to auditors and regulators.
— What skills will I gain from the "Artificial Intelligence (AI) for Treasury & Financial Decision-Making" course?
You will develop four core skills: financial data preparation and feature engineering, AI-based forecasting and predictive analytics, anomaly detection and document intelligence, and model evaluation with responsible AI governance.
— What tools, methods, or standards are covered in the "Artificial Intelligence (AI) for Treasury & Financial Decision-Making" course?
The course covers supervised, unsupervised, and reinforcement learning concepts, time series methods including moving average, exponential smoothing, and ARIMA basics, machine learning forecasting with regression and gradient boosting concepts, anomaly detection and classification models, clustering for segmentation, natural language processing (NLP) for contracts and financial documents, generative AI and prompt design, accuracy metrics such as MAE, RMSE, and MAPE, bias and drift checks, explainability and human review practice, backtesting and model monitoring, and AI governance with data protection and auditability controls.
— How is the "Artificial Intelligence (AI) for Treasury & Financial Decision-Making" course applied in real-world practice?
Participants apply it to real work such as building a cash flow forecast with two competing approaches, screening transactions for fraud patterns, extracting terms from supplier contracts, and preparing an AI adoption roadmap for a treasury function. The same methods support scenario stress testing and model validation.
Contact our team and let us help you find the right option.
Introduction
Artificial Intelligence (AI) for Treasury & Financial Decision-Making Training Course prepares finance professionals to use AI as a working tool rather than a headline. Participants work through data foundations, forecasting with machine learning, cash flow prediction, anomaly detection, and decision support as one connected practice. The programme connects AI capability with treasury and finance problems that actually recur, such as liquidity forecasting and fraud detection. As a result, learners gain a practical method for applying AI where it earns its place. Furthermore, the course strengthens analytical judgement, model literacy, and the governance that keeps AI use responsible.
Artificial Intelligence (AI) for Treasury & Financial Decision-Making Course Objectives
- Explain what AI, machine learning, and generative AI actually mean in finance.
- Identify treasury and finance use cases where AI adds measurable value.
- Prepare financial data for analysis: cleaning, structuring, and feature selection.
- Apply time series forecasting methods for cash flow and revenue prediction.
- Use classification and anomaly detection models for fraud and error detection.
- Apply natural language processing (NLP) to financial documents and contracts.
- Use generative AI tools for analysis, drafting, and scenario summarisation.
- Evaluate model accuracy with error metrics such as MAE, RMSE, and MAPE.
- Recognise bias, hallucination, and model risk in financial AI outputs.
- Apply AI governance, data protection, and auditability controls.
Course Methodology
- Interactive sessions built on treasury and finance AI use cases.
- Workshops on data preparation, forecasting, and anomaly detection.
- Group exercises on prompt design, model evaluation, and use case selection.
- Hands-on practice with forecasting models, scoring methods, and generative AI tools.
- Simulation of a fraud detection case and an AI-assisted decision review.
- Facilitator coaching and peer review on personal AI adoption plans.
Who Should Take This Course
- Treasury managers, analysts, and cash managers.
- Finance managers, controllers, and financial analysts.
- Financial planning and analysis (FP&A) professionals.
- Risk, credit, and internal control professionals.
- Data analysts supporting finance functions.
- Consultants advising on finance digitalisation and automation.
Artificial Intelligence (AI) for Treasury & Financial Decision-Making Course Outlines
AI Foundations for Finance Professionals
- • AI, machine learning, deep learning, and generative AI compared.
- • Supervised, unsupervised, and reinforcement learning in simple terms.
- • How AI differs from traditional financial modelling.
- • Treasury and finance use cases that deliver measurable value.
- • Data as the foundation: availability, quality, and ownership.
- • Common myths, overpromises, and realistic expectations.
- • Case discussion: an AI project that solved a problem nobody had.
Financial Data Preparation and Feature Engineering
- • Sourcing financial data: ERP, banking, market, and internal systems.
- • Data cleaning: duplicates, gaps, outliers, and inconsistencies.
- • Structuring time series data for forecasting.
- • Feature engineering: lags, seasonality, trends, and external drivers.
- • Data quality checks and validation before modelling.
- • Data protection and confidentiality in financial datasets.
- • Workshop: preparing a cash flow dataset for modelling.
Forecasting and Predictive Analytics
- • Time series methods: moving average, exponential smoothing, and ARIMA basics.
- • Machine learning forecasting with regression and gradient boosting concepts.
- • Cash flow forecasting and working capital prediction.
- • Revenue, cost, and demand forecasting applications.
- • Forecast accuracy metrics: MAE, RMSE, MAPE, and bias tracking.
- • Scenario simulation and stress testing with predictive models.
- • Exercise: building and comparing two forecasting approaches.
Detection, Classification, and Document Intelligence
- • Anomaly detection for fraud, error, and unusual transactions.
- • Classification models for credit risk and payment behaviour scoring.
- • Clustering for customer and cost segmentation.
- • Natural language processing (NLP) for contracts and financial documents.
- • Generative AI for analysis, drafting, and summarisation.
- • Prompt design and verification of generative AI outputs.
- • Simulation: investigating a suspicious transaction pattern with AI support.
Model Risk, Governance, and Decision Integration
- • Model risk: bias, hallucination, drift, and overfitting.
- • Explainability and the need for human review in decisions.
- • Validation, backtesting, and model performance monitoring.
- • AI governance, data protection, and auditability requirements.
- • Integrating AI outputs into treasury and finance workflows.
- • Building an AI adoption roadmap and capability plan.
- • Capstone: presenting an AI use case for a finance function.
Conclusion
By successfully completing Artificial Intelligence (AI) for Treasury & Financial Decision-Making, participants will gain a practical command of data preparation, forecasting, anomaly detection, and generative AI use in finance. They will acquire skills in model evaluation, AI-assisted cash flow prediction, fraud detection support, and responsible AI governance. Gentex Training Center designs the programme to help finance professionals apply AI where it earns its place.
FAQs
— What is the "Artificial Intelligence (AI) for Treasury & Financial Decision-Making" course about?
It prepares finance professionals to use AI as a working tool rather than a headline. The course spans AI foundations for finance, financial data preparation and feature engineering, forecasting and predictive analytics, detection, classification and document intelligence, and model risk, governance, and decision integration.
— What are the key benefits of the "Artificial Intelligence (AI) for Treasury & Financial Decision-Making" course?
You gain a practical method for applying AI where it earns its place. The course improves data preparation discipline, strengthens cash flow and revenue forecasting, speeds up fraud and anomaly detection, and gives you governance controls that make AI use defensible to auditors and regulators.
— What skills will I gain from the "Artificial Intelligence (AI) for Treasury & Financial Decision-Making" course?
You will develop four core skills: financial data preparation and feature engineering, AI-based forecasting and predictive analytics, anomaly detection and document intelligence, and model evaluation with responsible AI governance.
— What tools, methods, or standards are covered in the "Artificial Intelligence (AI) for Treasury & Financial Decision-Making" course?
The course covers supervised, unsupervised, and reinforcement learning concepts, time series methods including moving average, exponential smoothing, and ARIMA basics, machine learning forecasting with regression and gradient boosting concepts, anomaly detection and classification models, clustering for segmentation, natural language processing (NLP) for contracts and financial documents, generative AI and prompt design, accuracy metrics such as MAE, RMSE, and MAPE, bias and drift checks, explainability and human review practice, backtesting and model monitoring, and AI governance with data protection and auditability controls.
— How is the "Artificial Intelligence (AI) for Treasury & Financial Decision-Making" course applied in real-world practice?
Participants apply it to real work such as building a cash flow forecast with two competing approaches, screening transactions for fraud patterns, extracting terms from supplier contracts, and preparing an AI adoption roadmap for a treasury function. The same methods support scenario stress testing and model validation.
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