Assistant Professor in FinTech [Faculty Website]
School of Business, Stevens Institute of Technology
Stevens Institute for Artificial Intelligence
Altorfer 515F, 1 Castle Point Terrace, Hoboken, NJ 07030
E-mail: zyang99@stevens.edu
Phone: (+1) 201-216-5403
I am a Tenure-Track Assistant Professor in FinTech at the School of Business, Stevens Institute of Technology. I received a PhD in Data Science from the City University of Hong Kong in 2024 and a B.S. in Statistics from the Southern University of Science and Technology in 2020. My research agenda advances design science in information systems by designing and evaluating financial technologies that democratize sophisticated investing. I pursue two complementary approaches: designing platform mechanisms that lower sophistication barriers and developing AI-enabled decision support. Across both, I aim to make retail investing more accessible by shifting complexity from users to systems. In my leisure time, I enjoy playing tennis.
Feng Mai and I are co-chairing the FinTech Innovations minitrack at Hawaii International Conference on System Sciences (HICSS 2027) in beautiful Hawaii. We warmly invite you to submit your work to our minitrack!
In my FA690: Machine Learning in Finance course, I led a live demo with students showcasing how to build a course website using various Generative AI tools. Despite having no prior experience with HTML or CSS, we were able to create a fully functional and visually appealing site—something I’m genuinely proud of! The website hosts all lecture notes and podcast-style lecture (generated using Notebook LM). [Link to the Course Website]
I used Notebook LM to generate two podcast episodes based on two of my papers. The podcast sounds amazing! It really provides a new way of disseminating research. Check them out below:
Gaining a Seat at the Table: Enhancing the Attractiveness of Online Lending for Institutional Investors [Link to the Podcast]
Fair Pricing for All: A Generative Adversarial Pricing Framework for Risk-Commensurate Interest Rates [Link to the Podcast]
Hawaii International Conference on System Sciences (HICSS-60)
Hilton Waikoloa Village, Big Island, Hawaii | January 5-8, 2027
Track: Decision Analytics and Service Science | Minitrack: Fintech Innovations: AI, Analytics, and the Future of Financial Services
Minitrack Decription:
Financial services are being rapidly transformed by advances in data, algorithms, interactive AI, and decentralized infrastructures. From automated lending and robo-advisors to generative-AI-driven customer agents and real-time fraud detection across fiat and digital asset classes, fintech brings substantial opportunities for improved decision quality, personalization, and scale—and concomitant risks to fairness, transparency, and systemic stability.
Under the Decision Analytics and Service Science umbrella, this minitrack explores the intersection of fintech and analytics: how new models, agents, and interfaces change decision processes for firms, regulators, and consumers; how services are designed and delivered in digitally mediated and blockchain-enabled financial ecosystems; and what managerial, technical, and policy solutions can ensure trustworthy, robust, and inclusive outcomes.
We invite work that develops or evaluates methods for financial prediction, anomaly and fraud detection, credit scoring, algorithmic trading, risk assessment, and customer-facing financial agents, including applications of machine learning, generative AI/LLMs, multi-agent systems, and hybrid human-AI workflows. We also welcome research into explainability, auditability, and reproducibility of fintech models, evaluations of public and proprietary datasets, experiments on human-AI decision collaboration, and interdisciplinary studies of regulation, privacy, operational resilience, and social impact in both traditional banking and decentralized finance (DeFi). Contributions of all methods (analytical models, large-scale empirical studies, experiments, systems/demos, design papers, and policy analysis) are suitable.
This minitrack aims to foster dialogue between analytics researchers, service scientists, practitioners, and policy experts on how to deploy fintech innovations responsibly and effectively. We welcome papers covering a broad range of topics related to Fintech, AI, and financial analytics. Potential topics include, but are not limited to:
Generative AI in Finance: Applications of LLMs for financial forecasting, sentiment analysis, parsing financial statements, and automated reporting.
Agentic AI and Automation: The use of autonomous agents in trading, wealth management, and customer service operations.
Fraud Detection & Security: Advanced machine learning techniques for anomaly detection, anti-money laundering (AML), and identity verification (on-chain and off-chain).
Algorithmic Decision Making: The role of AI in high-frequency trading, portfolio optimization, and robo-advisory services.
Credit and Lending Analytics: Machine learning and deep learning for credit scoring, underwriting, and bias mitigation in lending.
Financial Service Innovation: Digital banking ecosystems, smart contracts, and programmable assets as service systems.
Open Data & Reproducibility: Research utilizing public datasets (e.g., SEC EDGAR, XBRL), open banking APIs, public ledger data, and comparative studies of proprietary vs. open data.
Explainable AI (XAI): Methodologies for ensuring transparency, auditability, and trust in AI-driven financial decisions.
Regulatory Technology (RegTech): Using AI to automate compliance, monitor market abuse, and interpret regulatory frameworks for traditional and crypto markets.
Human-AI Collaboration: How financial professionals (analysts, traders, advisors) collaborate with AI tools to enhance decision-making.
Minitrack Cochairs:
Feng Mai (feng-mai@uiowa.edu): Associate Professor & Henry B. Tippie Research Fellow, Department of Business Analytics, Tippie College of Business, The University of Iowa
Zonghao Yang (zyang99@stevens.edu): Assistant Professor in FinTech, School of Business, Stevens Institute of Technology
