Elite forensic neuroeconomist · Anti-fraud AI + bias in economic decisions · Governments • Central banks • Big Four • Fortune 500
Some economic decisions fail even with correct data.
The problem is usually human judgment under cognitive bias.
Behavioral auditing of decisions with economic, regulatory, or institutional impact. I identify biases, assumptions, and blind spots in executive committees before losses, sanctions, or irreversible errors.
Focus: reduce decision risk before execution.
If the cost of error is material, the diagnostic must be prior (not post-mortem).Deliverable: executive memo (1–2 pages) + mitigation checklist + decision-specific warning signals.
Confidential · Focused on critical decisions · Not public outreach or general training.
These were not technical failures. They were judgment failures under institutional pressure.
Documented casesBarings Bank · 1995
Overconfidence went unchallenged
Risk signals accumulated and were normalized by prior “success stories”. Authority bias + normalization of deviance in internal controls.
≈$1.3B · collapseUBS · 2011
Internal warnings repeatedly ignored
Signals were detected but filed away: the system “seemed under control”. Confirmation bias + groupthink in control functions.
$2.3B · lossesWells Fargo · 2016
Distorted incentives became normalized
The environment made misconduct feel “rational”. Rationalization + institutional conformity pressure.
$3.0B · settlementBoeing 737 MAX · 2018–19
Human factors were underestimated
Overreliance on automation and assumptions about behavior under pressure. Automation bias + organizational overconfidence.
$4.9B · chargeSelf-check
Could this be happening right now in your organization?
If you answer “yes” to two or more questions, decision risk is real and measurable.
Are decisions with economic or regulatory impact made without explicitly challenging the core assumptions?
Does the committee align quickly without deep, critical debate?
Are there weak signals that get normalized by institutional habit?
Is there excessive reliance on reports, models, or internal experts without independent challenge?
Is the cost of error assessed after deciding rather than before?
If you answered yes to ≥ 2
The risk isn’t hypothetical. It’s already inside the decision.
Cognitive biases are systematic and predictable, but invisible to decision-makers under institutional pressure. The most efficient intervention happens before the decision, not after the error.
Operating principle: reduce decision uncertainty and bias before execution.
Expert intervention
I identify judgment bias before it becomes losses or institutional errors
Behavioral audit of high-stakes economic decisions: mapping assumptions, incentives, dominant biases, and hidden behavioral risks in executive committees. I work where the cost of error is material: governance, risk, compliance, organizational fraud, regulatory decisions, and strategic operations.
“The question isn’t whether the committee can be wrong. It’s where, why, and how to reduce that risk before deciding.”
Intervention types
High-stakes decision diagnostics with executive traceability
Deliverables, criteria, and warning signals. Not “general conversations”.
Decision bias audit
Map of assumptions, dominant biases, and blind spots, with actionable mitigations for the executive committee.
Behavioral risk & organizational fraud
Human variables that degrade controls: crossed incentives, normalization of deviance, and hierarchical pressure.
AI, models & the human factor
Assessment of automation bias, overdependence on models, and design of effective human oversight.
Decision red-team
Controlled adversarial review: fragilities, extreme scenarios, unverified assumptions, and stress testing.
Strategic consult
How we work: executive deliverables, not general conversations
You’re not buying consulting hours. You’re buying actionable clarity on a decision with material risk.
Before: minimal structured context
I receive what’s needed to diagnose without improvisation.
- 1 concrete decision (not a “general topic”).
- 2–3 key documents (max 20 pages).
- Explicit definition of success and failure.
During: reducing decision uncertainty
A session designed to surface what the committee can’t see unaided.
- Assumption and dependency map.
- Dominant biases and mitigations.
- Risks, trade-offs, and adverse scenarios.
After: actionable deliverables
The diagnostic is documented so it can be executed.
- Executive memo (1–2 pages): diagnosis and recommendations.
- Mitigation checklist + decision-specific warning signals.
- Suggested monitoring metrics.
Example decision contexts (anglophone jurisdictions): U.S. (bank M&A approval under regulatory scrutiny; model risk governance for an F500), U.K. (conduct risk escalation & board challenge; audit committee decision under time pressure), Canada (OSFI-aligned risk appetite decision), Australia (ASIC/ APRA-facing control remediation prioritization), Singapore (MAS-regulated controls and fraud-prevention decision).
Fit: I engage only when the problem is real
I work only where there is capacity and willingness to implement.
- Defined economic, regulatory, or institutional impact.
- Minimum information access (no “black box”).
- Internal sponsor: someone can execute what’s agreed.
Access
If the decision has economic or institutional impact, analysis must be prior, not post-mortem
To request the diagnostic: (1) one concrete decision, (2) time horizon, (3) estimated economic or regulatory impact, (4) 2–3 key documents (max 20 pages).
Strategic consults from €3,000 (for decisions with material risk and confidentiality).
This page is designed to filter. It is not a public outreach channel or broad training offer: it is reserved for strategic decisions with economic, regulatory, or institutional impact.