Hands-On Artificial Intelligence for Banking

Hands-On Artificial Intelligence for Banking (versione in eBook)

A practical guide to building intelligent financial applications using machine learning techniques

di Subhash Shah, CFA Jeffrey Ng

Questo formato è la versione digitale in ebook.
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Real world financial applications with AI and production-grade data feed/technology About This Book Learn how to obtain financial data via Quandl or internal systems Automate commercial banking using Artificial Intelligence and Python programs Implement various Artificial intelligence models to make Personal Banking easy Who This Book Is For This book is best suited for machine learning engineers, data engineers, data scientist working in the finance industry who want to implement AI in their business applications. It would also help entrepreneurs, Venture Capitalists, Investment bankers, and wealth managers to understand how AI can help them solve different problems related to finance. Experience in the financial markets/banking domain is required. Basic knowledge of Python programming would also be beneficial. What You Will Learn Learn banking 101 in the lens of python Automate commercial bank pricing with Reinforcement Learning Perform Technical Analysis using Convolutional layers on Keras Use NLP to listen to market sentiment and visualize it using graph databases Deploy a robot advisor to manage your personal finance via open bank Unlock the history and future drivers of AI adoption in banking via coding examples Learn how to quality obtain financial data from Commerical, open and internal sources In Detail Finance needs a remake, and that starts with re-education by seeing through a new lens. This lens is Artificial Intelligence. If you get stuck with a boring job at a financial institution or wish to have a long-term career in finance, you need this book to survive and prepare yourself—given that you know how to write Python code. It shows you how finance works by applying AI to make financial services faster, personalized, efficient, and more accessible. It does not enforce the use case of following the typical sequences of a machine learning book; appropriate AI models and data processing techniques are derived instead.

Quarta di copertina

This book will be diving deep into the finance and banking industries. The author believes that an experienced data scientist finds the right solutions for the problems, not the other way around. By the end of the book, readers will be equipped to navigate the finance domain through the lens of AI.

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