About

Enterprise Operator. Trusted Advisor. Executive Educator.

I started my career as a nuclear engineer — trained to understand, model, and safely operate some of the most complex systems in the world. I bring that same systems thinking to banking, payments, risk, and AI: simplifying complex business and technology ecosystems into scalable models that perform under real-world and regulatory pressure.

That path led from nuclear engineering into quantitative finance and risk — building enterprise credit, capital, and stress-testing models, and then one of my bank's first AI/ML governance frameworks. From there I moved into technology, strategy, and innovation before taking the helm of enterprise payments.

Today I lead enterprise payments at a leading U.S. regional bank, supporting nearly $4T in annual transaction volume across consumer, corporate, wealth management, and treasury business lines. I work at the intersection of business performance, operations, technology, and risk — repositioning payments from a cost center into a product-led growth business and modernizing core infrastructure for real-time payments, embedded finance, and API-driven commercial banking.

In parallel, I have helped shape industry standards as former Chair of the FDX Commercial API Task Force and a Data & Trust Alliance working-group member. I teach applied AI and analytics at Duke Fuqua and UNC Kenan-Flagler, and advise founders, boards, and early-stage firms.

I also build with AI, not just about it. Over recent months I've prototyped real products with Claude Code and agentic workflows — building out personal applications that accelerate my productivity, adaptive learning apps and games for my kids, prototypes weekly for colleagues and potential clients, and this site. I run my own work on the same discipline I advise: re-allocate freed capacity to the highest-leverage work, meter the spend, and keep judgment in the loop. How I operate with AI →

My Leadership Point of View Download board biography (PDF)

Areas of expertise

Enterprise OperationsPayments & TreasuryAI Strategy & GovernanceDigital TransformationPlatform ModernizationOperating Model DesignRegulatory ExecutionExecutive Leadership

Education & credentials

PhD & BS, Nuclear Engineering — North Carolina State University
MBA, Corporate Finance — UNC Kenan-Flagler Business School
Chartered Financial Analyst (CFA) — CFA Institute

Teaching & faculty

10+ years in executive education — teaching applied AI, analytics, and decision-making to executives, MBA and graduate students, and functional leaders, translating enterprise experience into capability that holds up in regulated, real-world environments.

AI & Emerging Technology

Putting AI to work for measurable outcomes — with responsible adoption and oversight.

Data Science & Analytics

Turning data into decisions and operating advantage.

Operations & Business Modeling

Modeling operations, optimization, and scale.

Strategy & Decision-Making

Structured judgment and the economics of value.

Duke University — The Fuqua School of Business

Consulting Professor · 2021–2025

Courses: Advanced Data & Analytics (AI & Computer Vision) · Economics for Decision-Making

Designed and taught one of the industry's first executive-focused Applied AI programs; graduates went on to deploy AI with Google, AWS, and OpenAI across technology, manufacturing, and healthcare.

UNC Kenan-Flagler Business School

Coordinating Faculty · Adjunct Associate Professor · 2015–2025

Courses: Business Modeling · Analytics for Accounting

Coordinating faculty for analytics across the MBA@UNC and Master of Accounting programs; modernized the curriculum — machine learning, simulation, and decision modeling in Excel and Python — for the AI/ML era.

Academic service

Roles and Compensation CommitteeUNC Kenan-Flagler Business School	2020
Title and Advancement CommitteeUNC Kenan-Flagler Business School	2020
Admissions ProgramUNC Kenan-Flagler Business School	2015–2016

Awards & honors

#2-ranked courseDuke Fuqua — MSQM Program	2023
Nominated, Weatherspoon Teaching AwardUNC Kenan-Flagler Business School	2020
Mentored Teaching AwardNorth Carolina State University	2011
Outstanding Teaching Assistant AwardNorth Carolina State University	2010

Research

11 publications from doctoral and post-doctoral work in nuclear engineering — sensitivity analysis, perturbation theory, Monte Carlo methods, and reduced-order modeling. Full record on ORCID →

Show all 11 publications

Kennedy, C. B., Rabiti, C., & Abdel-Khalik, H. (2017). Generalized Perturbation Theory-Free Sensitivity Analysis for Eigenvalue Problems. Nuclear Technology.
Kennedy, C. B. (2015). GPT-Free Sensitivity Analysis for Reactor Depletion and Analysis. Doctoral dissertation, North Carolina State University.
Kennedy, C. B., & Abdel-Khalik, H. (2015). Depletion GPT-Free Sensitivity Analysis of the TMI Reactor Eigenvalue Model. International Conference on Physics of Reactors (PHYSOR 2014), Kyoto, Japan. doi:10.11484/jaea-conf-2014-003
Kennedy, C. B., & Abdel-Khalik, H. (2013). Depletion GPT-Free Sensitivity Analysis for Reactor Eigenvalue Problems. International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering (M&C 2013), Sun Valley, ID.
Abdel-Khalik, H. S., Bang, Y., Kennedy, C. B., & Hite, J. (2012). Reduced Order Modeling for Nonlinear Multi-Component Models. International Journal for Uncertainty Quantification, 2(4), 341–361. doi:10.1615/Int.J.UncertaintyQuantification.2012003523
Kennedy, C. B. (2012). Depletion GPT-Free Sensitivity Analysis for Eigenvalue Problems. Transactions of the American Nuclear Society.
Kennedy, C. B. (2012). GPT-Free Sensitivity Analysis for Eigenvalue Problems. Nuclear Technology.
Kennedy, C. B., Wu, Z., & Abdel-Khalik, H. (2012). Application of GPT-Free Method to Sensitivity Analysis in Monte Carlo Problems. American Nuclear Society 2012 Annual Meeting.
Kennedy, C. B., & Abdel-Khalik, H. (2011). On Rank Determination for Subspace Methods. Transactions of the American Nuclear Society.
Kennedy, C. B., & Abdel-Khalik, H. (2011). Perturbation Analysis for Monte Carlo Continuous Cross Section Models. International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering.
Kennedy, C. B., Abdel-Khalik, H., Palmiotti, & Rabiti, C. (2010). Sensitivity Analysis for Continuous Input Data Monte Carlo Models. Nuclear Reactor Technology.