NextGen FinTech Scholars: Data Science, AI & Digital Finance

Summer 2026

Location

Hybrid (primarily virtual, with optional in-person sessions at George Mason University Fairfax Campus)

Program Dates

Friday, June 26 – Friday, Sept 11, 2026 (12 weeks total)

  • Virtual Weekly Meetings: Fridays, June 26 – August 15, 2026 
  • Final Paper Refinement (No Meetings): August 15 – September 11, 2026
    (Students may request office hours by RSVP.)

Phase 1: Research & Learning: Weeks 1–7 | June 26 – August 7, 2026 Fridays, 12:00–2:00 PM ET / 9:00–11:00 AM PT

Students will participate in weekly live (synchronous) virtual sessions led by faculty and research mentors. Select holiday weeks may include asynchronous learning activities to provide additional flexibility. All sessions will be recorded and shared with participants, allowing students to stay engaged with the program while balancing summer travel, camps, or other commitments.

Phase 2: (Optional) Experience & Conference Day: Week 8 | August 13–15, 2026

  • Thursday, August 13 | 9:00 AM–5:00 PM ET (Optional -In Person Only)
    Industry Experience at Capital One Headquarters
    Students will visit Capital One Headquarters to engage with FinTech, AI, and data science professionals. The experience includes industry presentations, networking opportunities, and participation in a collaborative hackathon-style challenge focused on real-world financial technology applications.
  • Saturday, August 15 | 9:00 AM–2:00 PM ET (Optional – In Person or virtual)
    Research Presentation & Conference Day
    Students will have the opportunity to present their research projects and display their academic posters. Participation is optional and available in both in-person and virtual formats. Faculty members and industry professionals will review presentations, recognize outstanding work, and award scholarships and prizes to top-performing students and teams.

Phase 3: Research Paper Refinement & Publication Preparation: Weeks 9–12 | August 16 – September 11, 2026

  • No Scheduled Class Meetings
    Students will enter a dedicated four-week period to refine and finalize their scholarly research papers. Optional office hours will be available by RSVP for students seeking additional feedback or mentorship. This extended writing period allows participants to strengthen their analysis, improve their presentation of findings, and prepare a high-quality manuscript for publication consideration.
  • Final Research Paper Submission Deadline: Friday, September 11, 2026

Program Overview

This interactive Young Scholars Research Program introduces high school students to the rapidly evolving world of Financial Technology (FinTech), integrating Artificial Intelligence (AI), Machine Learning, and Data Analytics. Students will explore how tools such as AI platforms (including Claude and ChatGPT codex), Python, SAS, R, and Stata are used to analyze financial markets, interpret financial datasets such as CRSP, Compustat, and other datasets from SEC Edgar, Wharton Research Data services, Bloomberg, etc., and support real-world decision-making.

Through hands-on activities, real-world case studies, and a collaborative research project, students will gain foundational knowledge in Economics, Statistics, and Digital Finance, including emerging areas such as Cryptocurrency, Bitcoin, and Stablecoins. Students will also learn how to interpret Corporate Financial Statements and Annual Reports, bridging the gap between data analysis and business strategy. 

Key Learning Themes

  • AI in Finance: Applications of AI tools (e.g., Claude) in financial analysis and forecasting
  • Programming for Finance: Introduction to Python, R, and Stata for financial data analysis
  • Economics & Statistics: Understanding markets, risk, and data-driven decision-making
  • Financial Data Platforms: Exposure to professional and regulatory datasets and platforms
  • Corporate Finance Analysis: Reading and interpreting annual reports and financial statements
  • Digital Finance & Crypto: Bitcoin, stablecoins, and blockchain fundamentals
  • Case-Based Learning: Real-world financial case studies and industry applications

Students will complete a team-based research project and work on a scholarly paper. The final paper will be published on the Schar School Young Scholars Journals Webpage as well as the George Mason University (GMU) Library MARS Repository. 

Flyer for a “Three-Month Youth Research Internship” titled “NextGen FinTech Scholars: Data Science, AI & Digital Finance” at the Schar School of Policy and Government, George Mason University. Program dates are June 26–September 11, 2026. The flyer lists topics including fintech research and paper writing, AI in finance, economics and statistics, financial data platforms, corporate finance analysis, digital finance and crypto, and case-based learning. It highlights tools such as Claude, ChatGPT Codex, Pytho

Scholarships

Each winning team member from the conference will receive a $500 scholarship, which can be applied to any Young Scholars internship program at the Schar School during the academic years 2025 and 2026.

Research Projects Highlights and Features

Our Course Director, Dr. Lei Gao will lead students to work on the research projects, including but not limited to:

Introduction to FinTech and Data Analytics

  • Overview of FinTech and financial ecosystems: Definition, history, and evolution.
    • Fun introduction to FinTech: Definition and examples (e.g., mobile payments, online banking).
    • Group discussion: How do we use technology in our own financial lives?
  • Introduction to economics and statistics in financial decision-making; Key players in the FinTech landscape.
  • Applied Activity: Hands-on trading simulation using a mock account to practice buying and selling stocks

FinTech Trends & Research Project Development

  • Global trends in FinTech, AI, and digital finance
  • The role of AI in automating compliance processes; Current regulations affecting FinTech and AI applications.
  • Students begin group research projects using:
    • Financial datasets
    • Corporate reports
    • Economic analysis

AI, Claude, and Machine Learning Basics

  • Introduction to AI and machine learning in finance
    • Simple explanations of AI and ML with everyday examples (e.g., Netflix recommendations).
    • Fun quiz: Identify AI in our daily lives.
  • Using Claude and AI tools for financial insights and research
  • Applied Activity: Explore AI-driven financial recommendations; Create a simple budget using online tools.

Financial Data Analytics & Corporate Financial Performance

  • Introduction to Python, R, Stata, and basic AI/ML concepts for analyzing financial datasets
  • Understanding and interpreting corporate financial statements (10-K, annual reports) and key metrics (revenue, cost, growth).
  • Applied activity: Analyze a company using data visualization and simple models to evaluate financial performance.

About the Course Director

A man wearing a suit and tie smiles in a studio-style professional headshot.

Dr. Lei Gao is an associate professor of finance at George Mason University, where he teaches courses on corporate finance, investments, and empirical methods in finance. Gao has taught at Iowa State University, University of Memphis, University of Georgia, and Michigan State University. Gao is an active member of the American Finance Association and Financial Management Association. His research interests include market structure, investments, lending, corporate finance, and FinTech. His research has been published in top journals such as the Proceedings of the National Academy of Sciences of USA (PNAS), Journal of Financial Economics (JFE), and Journal of Financial and Quantitative Analysis (JFQA) among others. Gao's research has been featured in national news media, including ABC, Bloomberg, CNN, Financial Times, Fortune, NBC, Washington Post, and academic press, including ASSA, etc. His publication at PNAS about COVID-19 is one of the outputs of his COVID-19 project, which the CDC has adopted to forecast the mortality of COVID-19 in the U.S. and fight the pandemic. His publication at PNAS about Fair Lending is ranked by the Financial Times as #15 in its "Top 100 Business School Research with Social Impact" globally. Gao has given a speech at the Department of Housing and Urban Development (HUD) about fair lending. He has also been invited to be an expert witness before the U.S. Congress and his research has been the base for the testimony. His publication at JFE about "Market Intraday Momentum" is one of the most downloaded papers at SSRN in concurrent research. His research about short-selling regulation at JCF has been featured in the Cato Institute Research Brief. Gao has been a Financial Economist at the Securities and Exchange Commission (SEC), where he provides economics analysis and memos to support the Commission's enforcement and regulation, protecting investors; maintaining fair, orderly, and efficient markets; facilitating capital formation. Gao earned a Ph.D. in Finance from the University of Georgia, an M.S. in Industrial Math from Michigan State University, and a B.S. in Computer Science from Peking University.

Gao has served as a reviewer for the NSF CAREER Award and top academic journals, including American Economic Review: Insights, Management Science, Review of Finance, The Accounting Review, Journal of Corporate Finance, Journal of Banking and Finance, and Journal of Business Ethics, etc. Gao has delivered many presentations and talks at academic conferences, University seminars, Governments, and research institutions.