Hands-On Artificial Intelligence for Banking: A practical guide to building intelligent financial applications

October 31, 2020
Hands-On Artificial Intelligence for Banking: A practical guide to building intelligent financial applications

Delve into the world of real-world financialapplications using deep learning, artificial intelligence, andproduction-grade data feeds and technology with PythonKey FeaturesUnderstand how to obtain financial data via Quandl or internalsystemsAutomate commercial banking using artificial intelligence andPython programsImplement various artificial intelligence models to make personalbanking easyBook DescriptionRemodeling your outlook on banking begins with keeping up to datewith the latest and most effective approaches, such as artificialintelligence (AI). Hands-On Artificial Intelligence for Banking isa practical guide that will help you advance in your career in thebanking domain. The book will demonstrate AI implementation to makeyour banking services smoother, more cost-efficient, and accessibleto clients, focusing on both the client- and server-side uses ofAI.You'll begin by understanding the importance of artificialintelligence, while also gaining insights into the recent AIrevolution in the banking industry. Next, you'll get hands-onmachine learning experience, exploring how to use time seriesanalysis and reinforcement learning to automate client procurementsand banking and finance decisions. After this, you'll progress tolearning about mechanizing capital market decisions, usingautomated portfolio management systems and predicting the future ofinvestment banking. In addition to this, you'll explore conceptssuch as building personal wealth advisors and mass customization ofclient lifetime wealth. Finally, you'll get to grips with somereal-world AI considerations in the field of banking.By the end of this book, you'll be equipped with the skills youneed to navigate the finance domain by leveraging the power ofAI.What you will learnAutomate commercial bank pricing with reinforcementlearningPerform technical analysis using convolutional layers inKerasUse natural language processing (NLP) for predicting marketresponses and visualizing them using graph databasesDeploy a robot advisor to manage your personal finances viaOpen Bank APISense market needs using sentiment analysis for algorithmicmarketingExplore AI adoption in banking using practical examplesUnderstand how to obtain financial data from commercial, open,and internal sourcesWho this book is forThis is one of the most useful artificial intelligence books formachine learning engineers, data engineers, and data scientistsworking in the finance industry who are looking to implement AI intheir business applications. The book will also help entrepreneurs,venture capitalists, investment bankers, and wealth managers whowant to understand the importance of AI in finance and banking andhow it can help them solve different problems related to thesedomains. Prior experience in the financial markets or bankingdomain, and working knowledge of the Python programming languageare a must.Table of ContentsThe Importance of Artificial Intelligence in FinanceIndustryUsing Time Series Analysis to Automate Client ProcurementUsing Features and Reinforcement Learning to Automate BankFinancingMechanizing Capital Market DecisionsPredicting the Future of Investment BankersAutomated Portfolio Management Using Treynor Black Model andRestNetSensing Market Sentiment for Algorithmic Marketing atSell-SideBuilding Personal Wealth Advisers with Bank APIMass Customization of Client Lifetime WealthReal World Considerations