Richard Liu

Biography: After 25 years revolutionizing financial services through data science—securing three U.S. patents, handling trillion-dollar payment systems, and watching algorithms reshape global markets—Richard Liu has returned to academia with a mission: to solve the fundamental challenge that haunted every model built in industry. How do systems move beyond prediction to understanding? Beyond correlation to causation? Beyond black boxes to transparent intelligence?

Liu’s research lives at the intersection where statistical rigor meets AI innovation. His work develops graph-based knowledge systems that don’t just process transactions but understand the relationships driving them—teaching machines to see the financial world not as isolated data points but as an interconnected web of cause and effect, where a supply chain disruption in Taiwan ripples through payment networks to impact credit risk in Charlotte NC (second largest financial city in US) .

The causal inference frameworks emerging from his research combine experimental design principles that powered $30M cost reduction initiatives with modern machine learning’s pattern-finding capabilities. This hybrid approach proves crucial when randomized trials on financial markets are impossible, yet understanding what actually drives outcomes—versus what merely correlates—remains essential. His pending patent on adversarial LLM systems exemplifies this approach: ensuring AI reliability through rigorous statistical validation.

Every financial transaction tells a story. Throughout his career—from pioneering dynamic portfolio modeling at Bank of America to implementing real-time customer journey analytics at Wells Fargo—Liu has analyzed billions of these stories. His current research creates theoretical foundations to understand not just what happened, but why it happened and what will happen next. The work explores how digital footprints reveal behavioral mechanisms that traditional models miss, transforming transaction streams into windows of human decision-making.

His graph application research addresses a critical gap: while financial institutions have mastered data storage and processing, they struggle to represent and reason about complex relationships. Liu’s frameworks enable AI systems to navigate relationship networks—from counterparty risks to customer behavior patterns—making previously invisible connections computationally tractable.

In teaching Business Strategy with AI, Liu bridges the divide between technical capability and strategic deployment, sharing insights from implementing GPU-accelerated computing infrastructures and architecting Lake House data management systems that scaled to millions of users.

What drives this research is clear: AI has transformed industries, but its limitations remain profound. The next breakthrough won’t come from marginally better algorithms but from fundamentally rethinking how machines understand causality, context, and connections. That’s the frontier Liu explores at UNC Charlotte—where two decades of industry impact meets academic rigor to push the boundaries of intelligent systems.

Industry Leader turned Academic Explorer: 3 U.S. Patents | NVidia GTC Speaker | $1.3T in systems scaled

Degrees and Credentials:

• Master’s Degree: Northwestern University, Direct Marketing Database
• Bachelor’s Degree : National Taiwan University, Industrial Engineering

Research Interests:

• AI knowledge base and graph application
• Casualty with statistics design of experiment and machine learning approaches
• Financial transactions and digital activities

Courses Taught:

• Business strategy with AI

Dissertation Topic: TBD

Starting Year: Fall 2025

Expected Year of PhD Completion: 2028