Today, algorithms steer and inform more than 75% of modern trades. These mathematical constructs play an intricate role in automating processes, predicting market trends, optimizing portfolios, and fortifying decision-making in the financial domain. In an era where algorithms underpin the very foundation of financial services, it is imperative to hold a deep understanding of the intricate web of computational finance. Algorithmic Approaches to Financial Technology: Forecasting, Trading, and Optimization takes a comprehensive approach, spotlighting the fusion of artificial intelligence(AI) and algorithms in financial operations. The chapters explore the expansive landscape of algorithmic applications, from scrutinizing market trends to managing risks. The emphasis extends to AI-driven personnel selection, implementing trusted financial services, crafting recommendation systems for financial platforms, and critical fraud detection. This book serves as a vital resource for researchers, students, and practitioners. Its core strength lies in discussing AI-based algorithms as a catalyst for evolving market trends. It provides algorithmic solutions for stock markets, portfolio optimization, and robust financial fraud detection mechanisms. The text also casts a forward-looking gaze on the industry, predicting future trends while spotlighting the genetic algorithms in high-frequency trading, predictive analysis, and forecasting. Including research findings, case studies, best practices, and conceptual insights makes this book an indispensable reference for undergraduate, graduate, and executive students specializing in business, finance, economics, and technology. Beyond academia, the book finds resonance with industry professionals, policymakers, investors, and corporate executives, offering a nuanced understanding of the transformative impact of algorithmic approaches in shaping the future of financial technology.