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Microsoft Dynamics 365 AI for Business Insights

Microsoft Dynamics 365 AI for Business Insights

By : Dmitry Shargorodsky
4.4 (7)
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Microsoft Dynamics 365 AI for Business Insights

Microsoft Dynamics 365 AI for Business Insights

4.4 (7)
By: Dmitry Shargorodsky

Overview of this book

If there is one hot topic being discussed in every boardroom meeting today, it’s AI. With Microsoft Dynamics 365 AI proving to be a game-changer, it’s essential for business professionals to master this tool. Microsoft Dynamics 365 AI for Business Insights will help you harness AI across key business functions to streamline processes and enhance customer experiences. Written by a seasoned professional with 15+ years of experience, this book guides you through Dynamics 365 AI’s practical applications across sales, customer service, marketing, and finance departments. You'll learn how to enhance customer experiences, streamline sales processes, optimize marketing strategies, and improve financial forecasting. This book also explores the integration of generative AI tools such as OpenAI Service, Azure Open AI, language models, and Microsoft Copilot within the Dynamics 365 ecosystem. With real-world examples, case studies, and expert insights, you’ll discover the transformative potential of this powerful toolkit. As well as driving sales insights and implementing fraud protection, you’ll explore emerging AI trends, Microsoft's roadmap for Dynamics 365 AI, and the upcoming features. By the end, you’ll be all set to unlock new growth opportunities using Dynamics 365 AI.
Table of Contents (18 chapters)
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1
Part 1: Foundations of Dynamics 365 AI
4
Part 2: Implementing Dynamics 365 AI Across Business Functions
9
Part 3: Advanced Applications and Future Directions
14
Part 4: Looking Ahead

AI-driven fraud detection and prevention strategies

The application of AI in fraud detection within Dynamics 365 AI embodies a multifaceted approach, combining the nuanced capabilities of machine learning (ML), natural language processing (NLP), and predictive analytics to create a robust defense against fraudulent activities. Let’s explore these components in more detail and how they integrate into fraud prevention strategies.

Machine learning for pattern recognition

Pattern recognition involves identifying and classifying patterns in data. In the context of machine learning, these patterns could be anything from visual patterns in images, patterns in time-series data, or patterns in customer behavior.

  • Supervised learning: These ML models are trained on labeled datasets, learning to distinguish between fraudulent and legitimate transactions based on historical examples. This training enables the models to apply learned patterns to new transactions, effectively...

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