Guardado en:
| Autor principal: | |
|---|---|
| Formato: | Recurso digital |
| Lenguaje: | |
| Publicado: |
Zenodo
2025
|
| Acceso en línea: | https://doi.org/10.5281/zenodo.14853902 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866901157990891520 |
|---|---|
| author | Joshi, Satyadhar |
| author_facet | Joshi, Satyadhar |
| contents | <p>"Learn Agentically Powered Gen AI: Gen AI Agentic Framework for Financial Risk Management" by Satyadhar Joshi is a pioneering exploration of how Generative Artificial Intelligence (Gen AI) is revolutionizing financial risk management. This book bridges the gap between cutting-edge AI technologies and practical applications in finance, offering a comprehensive framework for leveraging Gen AI to enhance risk assessment, regulatory compliance, and decision-making processes. The book begins by introducing the transformative potential of Large Language Models (LLMs) like GPT-4 in financial systems. It highlights how Gen AI can analyze vast datasets, generate actionable insights, and simulate complex economic scenarios, enabling organizations to address challenges such as credit risk, market volatility, and fraud detection. Real-world case studies illustrate the integration of LLMs into financial workflows, showcasing their ability to improve accuracy and efficiency in risk modeling. A key focus is on credit risk management, where Gen AI complements traditional metrics like FICO scores by analyzing unstructured data such as loan descriptions and customer interactions. The book also explores market risk forecasting, demonstrating how AI-driven models outperform traditional methods in predicting economic trends. Additionally, it delves into anomaly detection, emphasizing the role of synthetic data and deep learning techniques in identifying fraud and system irregularities. The author proposes a robust Gen AI Agentic Framework, combining frontend tools for user interaction, backend models like fine-tuned GPT architectures, and hybrid AI systems that integrate traditional econometric models with Gen AI. This framework emphasizes scalability, adaptability, and regulatory compliance, ensuring AI-driven models are both reliable and interpretable. Challenges such as data scarcity, model bias, scalability, and regulatory alignment are addressed, with solutions like synthetic data generation, bias mitigation strategies, and explainable AI (XAI) techniques. The book also discusses ethical considerations, ensuring responsible AI deployment in financial systems. Written with the assistance of AI tools like ChatGPT, this book is a forward-thinking resource for professionals, researchers, and students. It not only advances understanding of Gen AI but also equips readers with the knowledge to navigate the evolving landscape of financial risk management. With plans for future editions featuring visual aids and expanded content, this book is a cornerstone for anyone looking to harness the power of AI in finance.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_14853902 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Gen AI for Market Risk and Credit Risk Joshi, Satyadhar <p>"Learn Agentically Powered Gen AI: Gen AI Agentic Framework for Financial Risk Management" by Satyadhar Joshi is a pioneering exploration of how Generative Artificial Intelligence (Gen AI) is revolutionizing financial risk management. This book bridges the gap between cutting-edge AI technologies and practical applications in finance, offering a comprehensive framework for leveraging Gen AI to enhance risk assessment, regulatory compliance, and decision-making processes. The book begins by introducing the transformative potential of Large Language Models (LLMs) like GPT-4 in financial systems. It highlights how Gen AI can analyze vast datasets, generate actionable insights, and simulate complex economic scenarios, enabling organizations to address challenges such as credit risk, market volatility, and fraud detection. Real-world case studies illustrate the integration of LLMs into financial workflows, showcasing their ability to improve accuracy and efficiency in risk modeling. A key focus is on credit risk management, where Gen AI complements traditional metrics like FICO scores by analyzing unstructured data such as loan descriptions and customer interactions. The book also explores market risk forecasting, demonstrating how AI-driven models outperform traditional methods in predicting economic trends. Additionally, it delves into anomaly detection, emphasizing the role of synthetic data and deep learning techniques in identifying fraud and system irregularities. The author proposes a robust Gen AI Agentic Framework, combining frontend tools for user interaction, backend models like fine-tuned GPT architectures, and hybrid AI systems that integrate traditional econometric models with Gen AI. This framework emphasizes scalability, adaptability, and regulatory compliance, ensuring AI-driven models are both reliable and interpretable. Challenges such as data scarcity, model bias, scalability, and regulatory alignment are addressed, with solutions like synthetic data generation, bias mitigation strategies, and explainable AI (XAI) techniques. The book also discusses ethical considerations, ensuring responsible AI deployment in financial systems. Written with the assistance of AI tools like ChatGPT, this book is a forward-thinking resource for professionals, researchers, and students. It not only advances understanding of Gen AI but also equips readers with the knowledge to navigate the evolving landscape of financial risk management. With plans for future editions featuring visual aids and expanded content, this book is a cornerstone for anyone looking to harness the power of AI in finance.</p> |
| title | Gen AI for Market Risk and Credit Risk |
| url | https://doi.org/10.5281/zenodo.14853902 |