Experience That Shapes

Better Decisions.

What holds Frieda Chung's attention is not any single model or program — it is that none of them sit still. Data shifts, techniques improve, technology changes, and policy and the people a program serves keep changing the questions. Across more than nineteen years, her craft has been refining rigorous analytical work against all of that, continuously, without losing sight of what it is actually for.

She is an analytical thinker with a creative streak — or the reverse, depending on the day. Her career has spanned federal credit modeling and governance at Deloitte and enterprise product leadership at Amazon — a range that taught her that technically sound work matters only when it is also understandable, usable, and built for the people relying on it. It also helps explain why she became a founder: she has always been drawn to the whole problem, not only one part of it.

She founded ALT to bring those instincts to the same table, and she remains accountable for the standard behind the work: is it asking the right question, will it withstand scrutiny, can the person relying on it act with confidence.

Outside the firm, Frieda has fostered more than twenty animals through the Lost Dog & Cat Rescue Foundation, and keeps her curiosity pointed in every direction — travel, hiking, and, most recently, learning about U.S. Air Force aircraft.

Mark Hutson has spent more than fifteen years inside the models that federal credit programs run on — building them, stress-testing them, and being the person brought in to find out whether they actually hold up. It is unglamorous, exacting work, and it is the work he's best at.

A financial economist by training, he pairs PhD-level econometric rigor with a practical instinct for what a model will do once it leaves the whiteboard and meets real portfolio data, an auditor, and a budget cycle. Over his career he has validated, enhanced, or rebuilt credit and insurance models across a wide stretch of the federal landscape — student loans, mortgage insurance, and programs at agencies ranging from FDIC to the Export-Import Bank — including independent validation work later reflected in a GAO review. He founded and led the modeling, statistics, and data science group at Summit Consulting.

His view of machine learning is the same as his view of any method: use it where it earns its place. He builds explainable, audit-defensible models — gradient boosting, ensemble methods, time-series forecasting, Autometrics — not for their own sake, but because they make a forecast sharper or a risk clearer while still surviving the scrutiny federal work demands.

Outside the firm, Mark has served on several non-profit boards and regularly volunteers as a nationally-certified USA Swimming official.