Most finance problems are not about producing more numbers, they are about turning the numbers into a decision. Below are the areas where I do that work, the problem each one addresses, how I approach it, and the tools that already exist to help.
Leadership gets reports, but not understanding. The numbers arrive late, bury the signal in detail, and don't connect to the decisions on the table.
How I approach it
I build reporting that starts from the decisions it needs to support, clean monthly P&L, relevant KPIs by segment and product, and variance reporting that explains the "why," not just the "what." The goal is a package leadership can read in minutes and act on with confidence.
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The forecast is a single number nobody fully believes. When conditions change, there's no structured way to see what moves, or which assumptions actually matter.
How I approach it
I build driver-based forecasts with explicit scenarios and sensitivity analysis, so leadership sees a realistic range rather than a false point estimate, and knows which two or three assumptions deserve the most attention. Forecasting becomes a planning conversation, not a spreadsheet ritual.
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Revenue is up but margin isn't, and nobody can say exactly why. Pricing and mix decisions get made on instinct, without seeing the full tradeoff.
How I approach it
I decompose margin into price, volume, mix, and cost so the real driver is unambiguous, then model pricing with elasticity and contribution to show where pricing power genuinely exists. The output is a defensible recommendation, not a hunch.
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AI tools are everywhere, but most finance teams either ignore them or trust them too much. Neither is safe, and neither captures the real leverage on offer.
How I approach it
I design workflows that use AI tools, including Claude, where they genuinely help, structuring problems, drafting commentary, documenting models, accelerating analysis, while keeping financial judgment human. I've benchmarked these tools on real FP&A work, so the guidance is evidence-based, not hype.
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A real decision, entering a market, changing pricing, adding capacity, involves revenue, margin, risk, and cash at once. Treating them separately produces answers that fall apart under scrutiny.
How I approach it
I build integrated decision models that make the tradeoffs explicit, drawing on a background in econometrics and optimization. The result is a structured view of the options and their consequences, so the choice is informed rather than intuitive.
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The off-the-shelf tool gets you most of the way, but your business has structure a template can't capture, and your team needs something they can own and maintain.
How I approach it
I build to your chart of accounts, drivers, and reporting needs, in Excel, Power BI, or an AI-assisted workflow, and document it so your team can run and extend it. The aim is a tool that fits how you actually work, not one you have to bend yourself around.
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Describe the decision you're trying to make or the question your analysis needs to answer. A rough description is enough, I'll tell you how I'd approach it and which tools apply.