Название: Generative AI in Banking Financial Services and Insurance: A Guide to Use Cases, Approaches and InsightsАвтор: Anshul Saxena, Shalaka Verma, Jayant Mahajan
Издательство: Apress
Год: 2024
Страниц: 322
Язык: английский
Формат: pdf, epub
Размер: 10.1 MB
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.
GANs have been used in various applications, including image generation, style transfer, and super-resolution. Several variants of GANs have been developed to address specific challenges and improve performance:
• Conditional GANs (cGANs): Introduce additional information (e.g., class labels) to both the generator and discriminator, allowing for conditional data generation.
• CycleGAN: Enables image-to-image translation without requiring paired training examples, useful for tasks like converting images from one domain to another (e.g., photos to paintings).
• Wasserstein GANs (WGANs): Modify the training objective to address instability in the training process, leading to more stable and reliable convergence.
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 sector
Who This Book Is For:
Professionals in the BFSI and IT sectors, including system administrators and programmers.
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