I don't start with opinions. I start with the data.
Most expensive revenue problems don't trace back to one obviously broken report. They grow in the handoffs between people, platforms, rules, and systems.
A payment decline can start with a configuration change. A reporting gap can come from an interface. A growing balance can be bad routing rather than poor follow-up. If you only look at the final symptom, you rarely see the full picture.
The way I work is straightforward. Follow the transaction. Reconcile every system it touches. Prove where the process you expected diverged from the one that actually ran.
1. Define the symptom
Get clear on what changed, when it changed, how the issue was first noticed, and what financial or operational result looks wrong.
The first complaint is where I start. It's almost never where I stop.
2. Validate the data
Before I draw conclusions from a dashboard or spreadsheet, I confirm it actually reflects what happened. If the reports don't agree, one of them is wrong, and I want to know which one before I go any further.
3. Map the full workflow
I document how a transaction is created, transformed, routed, processed, reported, and eventually paid or resolved. That includes the systems, business rules, manual steps, integrations, and every handoff along the way.
4. Reconcile across systems
I compare what each system received, stored, transmitted, and reported. If two systems disagree, I want to know why. That's usually where the money is.
5. Isolate the failure
I test competing explanations until the actual mapping issue, configuration error, workflow gap, reporting blind spot, or business-rule failure can be demonstrated. The conclusion rests on evidence, not assumption.
6. Quantify the impact
I measure the affected volume, financial exposure, timing, and recoverability wherever the data allows. That's what separates a handful of exceptions from a real, repeating problem.
7. Build a practical correction plan
I spell out what needs to change, who should own it, how the correction can be validated, and what control should catch it if it comes back.
Finding the problem isn't the finish line. I want to help keep it from happening again.
The method stays the same, even when the systems don't
My deepest experience is in healthcare revenue cycle, laboratory billing, financial operations, and multi-system reconciliation. The terminology shifts from one organization to another. The investigative questions don't. What should have happened? What actually happened? Where did the two diverge? What did the difference cost?
Related reading: Case Studies, Revenue Recovery Scan, and Root Cause Diagnostic.
Something in the numbers not adding up?
You don't need to know the cause before reaching out. Start with the symptom, the systems involved, and what changed. I'll help figure out where the investigation should begin.