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AI Forecasting in FP&A: How to Predict Cash Flow, Revenue, and Cost Drivers with ML: Step into the future of finance. Predict more. React less.
Reactive Publishing
In an era defined by rapid change and data overload, traditional forecasting methods no longer cut it. AI Forecasting in FP&A arms finance professionals, analysts, and CFOs with the tools to evolve—from reactive number crunchers to strategic AI-powered decision-makers.
Written by financial strategist Hayden Van Der Post, this hands-on guide demystifies how machine learning can transform financial planning and analysis. You’ll discover practical techniques to forecast cash flow, model revenue with precision, and decode cost behavior using real-world data and Python-based ML tools.
Inside you’ll
How to build dynamic, self-improving forecasting models with supervised learning
Techniques to model seasonal, cyclical, and irregular financial patterns
Tools to integrate external drivers (macroeconomic data, customer trends, etc.) into forecasts
Methods to reduce bias, improve accuracy, and justify model predictions in executive settings
How to move from static spreadsheets to scalable, automated forecasting systems
Whether you're a startup FP&A lead or an enterprise finance executive, this book delivers a roadmap to modern forecasting—where machine learning meets financial intelligence.
Step into the future of finance. Predict more. React less.
In an era defined by rapid change and data overload, traditional forecasting methods no longer cut it. AI Forecasting in FP&A arms finance professionals, analysts, and CFOs with the tools to evolve—from reactive number crunchers to strategic AI-powered decision-makers.
Written by financial strategist Hayden Van Der Post, this hands-on guide demystifies how machine learning can transform financial planning and analysis. You’ll discover practical techniques to forecast cash flow, model revenue with precision, and decode cost behavior using real-world data and Python-based ML tools.
Inside you’ll
How to build dynamic, self-improving forecasting models with supervised learning
Techniques to model seasonal, cyclical, and irregular financial patterns
Tools to integrate external drivers (macroeconomic data, customer trends, etc.) into forecasts
Methods to reduce bias, improve accuracy, and justify model predictions in executive settings
How to move from static spreadsheets to scalable, automated forecasting systems
Whether you're a startup FP&A lead or an enterprise finance executive, this book delivers a roadmap to modern forecasting—where machine learning meets financial intelligence.
Step into the future of finance. Predict more. React less.
574 pages, Kindle Edition
Published May 2, 2025
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