Krishna Chaitanya Yarlagadda is an accomplished Data Science and Artificial Intelligence leader with over 13 years of experience in machine learning, predictive analytics, business intelligence, risk modeling, and cloud-based analytics solutions. Throughout his career, he has successfully led large-scale analytics and AI initiatives across industries including financial services, e-commerce, healthcare, and marketing analytics. He has worked with globally recognized organizations such as Amazon, JP Morgan Chase & Co., Mercury Financial, and Atlanticus, where he developed innovative AI-driven solutions that improved operational efficiency, enhanced customer engagement, optimized forecasting, and generated multi-million-dollar business impact. His technical expertise includes AWS cloud technologies, Python, SAS, Tableau, SQL, machine learning, predictive modeling, data engineering, and advanced statistical analytics. Currently serving as Director of Data Science & AI at Atlanticus, Krishna leads end-to-end AI/ML strategy development for underwriting, risk analytics, and model governance. He has built scalable machine learning frameworks, automated model monitoring systems, and introduced Generative AI capabilities to improve business intelligence and risk transparency. During his tenure at Amazon, he played a key role in developing AI-powered dashboards, forecasting optimization systems, and predictive analytics solutions that streamlined decision-making processes and drove significant revenue growth. His innovative work has earned him multiple professional recognitions, including prestigious technology and innovation awards for excellence in machine learning, analytics, and AI-driven business transformation. Beyond his corporate achievements, Krishna is an active contributor to the global technology and research community. He regularly serves as an international conference speaker, session chair, peer reviewer, hackathon judge, and journal reviewer in the fields of Artificial Intelligence, Data Science, Cybersecurity, and Intelligent Systems. He has presented at numerous global conferences on topics such as AI-powered risk modeling, supply chain optimization, data storytelling, and enterprise AI strategies. Krishna also holds several globally recognized certifications, including AWS Certified Machine Learning Specialty, Tableau Certifications, Microsoft Azure Fundamentals, and multiple SAS certifications. Passionate about innovation and continuous learning, he remains dedicated to advancing AI technologies that solve real-world business challenges and create meaningful impact across industries.  

Scaling AI in FinTech: From Experimentation to Production SystemsArtificial Intelligence has rapidly evolved from an experimental capability into a core driver of innovation across the FinTech ecosystem. While many organizations successfully build promising machine learning prototypes, the real challenge lies in scaling these solutions into reliable, production-grade systems that can support business growth, regulatory expectations, and real-time decision making.This session explores the practical journey of operationalizing AI in financial services—from model development and experimentation to enterprise deployment and long-term governance. The discussion will cover key challenges including data quality, model drift, explainability, infrastructure scalability, risk management, and cross-functional alignment between data science, engineering, and business teams. Drawing from real-world industry experience in large-scale analytics and AI-driven decision systems, the session will also highlight how modern FinTech organizations are leveraging predictive analytics, automation, and emerging GenAI capabilities to improve fraud detection, credit risk assessment, customer engagement, and operational efficiency. Attendees will gain practical insights into building scalable AI ecosystems that move beyond proof-of-concept models and deliver measurable business impact while maintaining transparency, trust, and responsible AI practices in highly regulated environments.