Academic research at the intersection of government accounting, agentic AI systems, and regulatory compliance — helping public agencies modernize without compromising oversight.
As a pioneer in Agentic AI for auditing and creator of the Hybrid Agentic Audit (HAA) framework, focus on redefining financial operations, shifting from manual data processing to continuous, explainable audit intelligence, while maintaining rigorous compliance standards and human-in-the-loop governance.
Bridges academic research in accounting information systems with practical for companies navigating the transition to AI-augmented finance operations. I focus specifically on the structures, safeguards, and standards needed to make agentic automation trustworthy in public sector contexts.
Research-backed engagements designed around the specific needs, risk tolerance, and regulatory obligations of public sector and private companies finance teams.
Help companies and agencies identify, map, and eliminate manual data entry workflows using the HAA Framework's agentic ingestion layer. From reconciliation to grant fund tracking, Design automated pipelines that connect your existing sources — ERPs, PDFs, Excel, databases — to normalized, audit-ready outputs with zero manual transcription.
Every government entity has a unique combination of data sources, regulatory obligations, and staff capacity. Design a bespoke Hybrid Agentic Audit implementation — specifying which agents to deploy, how Institutional Memory is built, where the Human-on-the-Loop checkpoints sit, and how the XAI traceability trail is structured for your auditors.
The finance software market is highly saturated, with numerous vendors competing to address GASB, FAA, federal, and local compliance requirements. Provide independent, research-based software selection guidance — evaluating tools against your compliance obligations, data architecture, staff skill level, and long-term interoperability needs. No vendor relationships, no commissions.
Deliver structured training workshops and curriculum materials for finance staff, covering the intellectual foundations of agentic AI in accounting, regulatory compliance literacy (GASB, FAA, OMB), and the human judgment skills needed to oversee autonomous systems responsibly. Based on my academic "Philosophies in Accounting" curriculum series.
A multi-agent architecture where six specialized AI agents — coordinated by an orchestration layer and grounded in Institutional Memory — continuously auditfinancial data, escalating only meaningful exceptions to the Human-on-the-Loop.
The HAA framework's reasoning engine enforces these standards automatically — not as a checklist, but as embedded regulatory logic that agents apply to every transaction.
Research, writing, and curriculum development building the intellectual foundation AI in finance — for practitioners, researchers, and the next generation of accountants.
The case for preserving human judgment at the apex of every automated workflow, and why full autonomy creates unacceptable fiduciary and compliance risk in finance.
How six specialized agents — each with a defined domain — coordinate through shared Institutional Memory to achieve 100% population analysis at costs traditional sampling cannot match.
Reframing automation as capacity expansion — freeing accountants from data entry so they can focus on analysis, judgment, and policy-aligned financial stewardship.
Explainable AI isn't optional in the public sector. This paper defines minimum traceability standards for agentic systems operating in federally regulated financial environments.
Traditional audit theory relies on detective controls applied to samples. Agentic fraud monitoring shifts the model to preventive, real-time intervention across entire populations.
A structured curriculum preparing finance professionals for agentic tools — covering AI oversight principles, regulatory fluency, data literacy, and ethical fiduciary judgment.