دانلود کتاب هوش مصنوعی در تصمیم‌گیری مالی

Artificial Intelligence (AI) and Machine Learning (ML) are transforming financial decision-making. Join us as we demonstrate that advanced analytics no longer requires complex coding knowledge or high-end systems. With Lo-code tools and accessible Python libraries, finance professionals can now apply powerful techniques using just their laptops. This chapter sets the stage for the application of AI/ML in financial decision-making. Financial decision-making involves a structured process where financial resources controlled by an entity are utilized to maximize wealth. This wealth ultimately belongs to the shareholders. The decision-making process has evolved over the years, becoming quicker, more efficient, and broader in scope. Automation and the use of advanced analytics in the decision-making process have gained prominence in recent years. AI and ML are two major developments that have influenced the financial decision-making process. AI broadly refers to the capability of systems to perform tasks that typically require human intelligence. These would include reasoning, problem-solving, understanding language, perceiving environments, and other similar tasks. ML, on the other hand, is a subset of AI that involves training algorithms to learn patterns from data and make predictions or decisions without being explicitly programmed for each task. This does not mean that there is no programming involved. The main computational complexities are built into system resources. Coding is simply limited to invoking the resource and feeding it with data. The performance of these models improves over time as they process more and more data. The integration of AI and ML with financial decision-making is transforming the industry. It allows faster, more informed, and often more accurate assessments of risk, opportunity, and market behavior. Lo-code platforms are making these advanced analytical techniques accessible to finance professionals without programming expertise. They are lowering the knowledge barrier by providing intuitive drag-and-drop interfaces, pre-built models, and automated workflows. This has allowed finance professionals to move away from rigid, rule-based processes to more flexible, data-driven decision-making. In this book, we will closely examine business issues that can be addressed by various AI and ML tools. Do not worry, you do not need to be an expert coder to use these tools, and as long as you have a thorough understanding of the domain, you are good to go.