Böcker av Anshul Saxena

This book explores the integration of Generative AI within the Banking, Financial Services, and Insurance (BFSI) sector, elucidating its implications, applications, and the future landscape of BFSI.The first part delves into the origins and evolution of Generative AI, providing insights into its mechanics and applications within the BFSI context. It goes into the core technologies behind Generative AI, emphasizing their significance and practical applications. The second part explores how Generative AI intersects with core banking processes, ranging from transactional activities to customer support, credit assessment, and regulatory compliance. It focuses on the digital transformation driving investment banking into the future. It also discusses AI¿s role in algorithmic trading, client interactions, and regulatory adaptations. It analyzes AI-driven techniques in portfolio management, customer-centric solutions, and the next-generation approach to financial planning and advisory matters. The third part equips you with a structured roadmap for AI adoption in BFSI, highlighting the steps and the challenges. It outlines clear steps to assist BFSI institutions in incorporating Generative AI into their operations. It also raises awareness about the moral implications associated with AI in the BFSI sector.By the end of this book you will understand Generative AI¿s present and future role in the BFSI sector.What You Will Learn Know what Generative AI is and its applications in the BFSI sector Understand deep learning and its significance in generative models Analyze the AI-driven techniques in portfolio management and customer-centric solutions Know the future of investment banking and trading with AI Know the challenges of integrating AI into the BFSI sectorWho This Book Is ForProfessionals in the BFSI and IT sectors, including system administrators and programmers

Elevate your problem-solving prowess by using cutting-edge quantum machine learning algorithms in the financial domainPurchase of the print or Kindle book includes a free PDF eBookKey FeaturesLearn to solve financial analysis problems by harnessing quantum powerUnlock the benefits of quantum machine learning and its potential to solve problemsTrain QML to solve portfolio optimization and risk analytics problemsBook DescriptionQuantum computing has the potential to revolutionize the computing paradigm. By integrating quantum algorithms with artificial intelligence and machine learning, we can harness the power of qubits to deliver comprehensive and optimized solutions for intricate financial problems.This book offers step-by-step guidance on using various quantum algorithm frameworks within a Python environment, enabling you to tackle business challenges in finance. With the use of contrasting solutions from well-known Python libraries with quantum algorithms, you'll discover the advantages of the quantum approach. Focusing on clarity, the authors expertly present complex quantum algorithms in a straightforward, yet comprehensive way. Throughout the book, you'll become adept at working with simple programs illustrating quantum computing principles. Gradually, you'll progress to more sophisticated programs and algorithms that harness the full power of quantum computing.By the end of this book, you'll be able to design, implement and run your own quantum computing programs to turbocharge your financial modelling.What you will learnExamine quantum computing frameworks, models, and techniquesGet to grips with QC's impact on financial modelling and simulationsUtilize Qiskit and Pennylane for financial analysesEmploy renowned NISQ algorithms in model buildingDiscover best practices for QML algorithmSolve data mining issues with QML algorithmsWho this book is forThis book is for financial practitioners, quantitative analysts, or developers; looking to bring the power of quantum computing to their organizations. This is an essential resource written for finance professionals, who want to harness the power of quantum computers for solving real-world financial problems. A basic understanding of Python, calculus, linear algebra, and quantum computing is a prerequisite.Table of ContentsQuantum Computing ParadigmQuantum Machine Learning AlgorithmsQuantum Finance LandscapeDerivatives ValuationPortfolio ValuationsCredit Risk AnalyticsImplementation in Quantum CloudsHPCs and Simulators RelevanceNISQ Quantum Hardware EvolutionQuantum Roadmap for Banks and Fintechs