25+ years
CFO-level financial judgment
FP&A · valuation · M&A · investment decisions
Test whether AI conclusions make financial sense.
JOÃO MARCELO ALVES / SENIOR FINANCE EXECUTIVE
I bring 25+ years of CFO-level financial judgment to evaluating AI-generated analysis—reconciling the numbers, challenging unsupported reasoning and making evidence-based scoring decisions.
CFO-level financial judgment
FP&A · valuation · M&A · investment decisions
Test whether AI conclusions make financial sense.
Source-driven analysis
Financial reconciliations, critical assumptions and reporting integrity
Detect unsupported claims and source inconsistencies.
Built evaluation-ready frameworks
LLM-assisted analysis and a 30-test accounting control framework
Define ground truth, rubrics and critical-failure checks.
01 / THE EVIDENCE
Two finance AI evaluation cases built around observed development outputs. Each makes the task, reference evidence, scoring logic and expert adjudication inspectable.
T4R Financial Decision Intelligence
Review cash-flow reconciliation and runway claims against recorded figures, classifications and assumptions. See where plausible results must be qualified or held.
T4R Sentinel Control
Review observed test coverage, source lineage and independent verification status. See why an accurate count is not the same as full accounting assurance.
Evidence standard: the cases analyze recorded historical development outputs. Their evaluator dispositions and scoped scores are simulated expert adjudications—not controlled benchmark results for named LLMs.
02 / HOW I EVALUATE
I use an evidence-first, five-step workflow across financial AI models. The task, source data and scoring criteria adapt to each use case; the standard of proof stays consistent.
Identify the model’s task, intended decision and material financial failure modes.
Reconcile source records, reproduce key calculations and define evidence limits.
Specify observable checks, weights, acceptance thresholds and critical-failure gates.
Run documented test cases, preserve raw outputs and compare claims with the reference.
Apply evidence-backed scores; identify critical errors, decision risk and release conditions.
Keep the evaluation sequence; tailor the inputs, rubric and failure gates.
Cash-flow evidence · Assumptions · Decision quality
Reconciliation · Test coverage · Source integrity
03 / THE DOMAIN EXPERT
I have been accountable for financial reporting, investment analysis and executive decisions. That experience tells me which AI errors matter, which conclusions overreach the evidence and when a result is not decision-ready.
SELECTED FINANCIAL TRACK RECORD
Supported reporting readiness and financial-evidence review for a BRL 48.7M transaction.
Supported financial strategy for BRL 14M in equity and innovation financing.
Reduced a financial reporting cycle from 50 to 20 days through process and system integration.
My evaluation standard: Can I reproduce the number, defend the reasoning and trust the decision?
04 / LET'S TALK
Finance AI model evaluation · Expert review · English (C1) / Portuguese · International remote