Jitender Jain of Cognizant

This discussion will delve into how Artificial Intelligence (AI) is transforming the financial industry through advanced data extraction, automation, and predictive analytics. This presentation covers key industry use cases, including fraud detection, customer service automation, and AI-driven personalization in financial products. A case study on AI-augmented income verification demonstrates real-world applications of intelligent data processing. Additionally, best practices for implementing AI in FinTech enterprises are discussed, addressing challenges such as data privacy, regulatory compliance, and model deployment. The session concludes with insights into emerging AI trends and their long-term impact on financial technology and enterprise automation.  

Biography:

Jitender Jain is a seasoned technical engineer with 16+ years of expertise in software architecture, AI, cloud computing, and distributed systems. He has led large-scale digital transformation initiatives in fintech holding key roles such as Principal Software Engineer at a leading U.S. bank.

An AWS Certified Solutions Architect with a USPTO-granted patent (US11893819) to advance Fintech with AI, Jitender has built AI-driven platforms that enhanced scalability, automation, and cost efficiency. He has a strong academic foundation in Computer Science and Engineering and actively contributes to white papers, open-source projects, and technical publications.

As a member of the Forbes Technology Council, Jitender shares industry insights into AI, cloud computing, influencing technology leaders and driving innovation while mentoring engineering teams to push technological boundaries. He is also an experienced speaker who has presented at numerous conferences on Technology, AI, and Finance.

Attendees will receive access to a resource packet containing reference materials on AI implementation frameworks, relevant case studies, and code samples for common FinTech AI applications. 

Medha Gupta of Beyond

This presentation explores the implementation of data-driven dynamic pricing systems in e-commerce using BigQuery, SQL, and dbt. I'll demonstrate how to unify disparate data streams—including transactions, competitor intelligence, and inventory metrics—into responsive pricing models. The session covers developing data pipelines that transform commercial data into pricing signals and creating measurement frameworks that evaluate strategy effectiveness beyond simple revenue metrics. Attendees will gain practical understanding of how modern data warehousing enables pricing strategies that balance profitability with customer relationships in today's volatile market.

Biography:

An accomplished data engineer with over 14 years of industry experience, the speaker has extensive expertise in building sophisticated data systems for e-commerce platforms. Her career includes significant tenures at Dell and IBM, where she developed data warehousing solutions supporting mergers and acquisitions as well as telecom commerce operations.

Specializing in data-driven business solutions, she possesses deep technical knowledge in SQL and modern data engineering practices. Her work has focused on transforming complex data ecosystems into actionable business intelligence, particularly in dynamic pricing and market analysis environments.

The speaker has presented her knowledge and insights at numerous national and international conferences. As a Senior Member of the IEEE Computer Society, she actively contributes to advancing the field's professional standards and knowledge base through her continued participation in IEEE activities.