SE Press Announces Publication of "Cognitive Risk Mitigation in Financial Decision Systems"

Date: July 17, 2025

Authors: Paul Falconer & ESAsi, Scientific Existentialism Press

OSF Repository Link: https://osf.io/x7jzy

Overview

SE Press is pleased to announce the release of a new foundational paper on the Open Science Framework:"Cognitive Risk Mitigation in Financial Decision Systems." This protocol-driven framework integrates metacognitive self-assessment, adversarial validation, and dynamic harm scoring to address systemic risks in AI-powered finance. All protocols, benchmarks, simulation code, and governance templates are openly published for audit, replication, and regulatory review.

Key Highlights

• Innovative Protocol Design:The paper details how introspective checks, adversarial scenario testing, and dynamic harm blocks can reduce bias, limit model drift, and increase explainability—directly within financial decision pipelines.

• Simulation-Based Results:

  • 96.1% drift detection rate

  • 1.8% of flagged high-risk actions, compared to 12.2% for standard AI

  • 0 regulatory exceptions in simulations

  • Bias and fairness metrics logged throughout, with a 30% simulated reduction in compliance costs

• Governance and Audit:Includes governance committee templates, role schedules, and audit log methods mapped to emerging global standards such as the EU AI Act and Basel III.

Results Table

Metric	ESAsi Protocol	Standard AI	Rule-Based Baseline
Bias Leakage Recall	0.13	0.44	0.51
Demographic Parity Gap	0.07	0.16	0.20
Drift Detection (%)	96.1	72.2	60.9
Adversarial Robustness	0.91	0.68	0.55
Regulatory Faults/run	0	5	9
Explainability Score	0.80	0.39	0.61

Metrics derived from simulation-based testing; code and audit templates included with publication.

Epistemic Note

• Confidence: 77% (simulation-based, not yet field-validated)

• Reasoning: Inductive, protocol-driven audit, open for adversarial and regulatory review

• Plausibility: 0.88 (simulation outperforms baselines)

• Harm: 0.09 (no simulated high-risk actions unmitigated)

• Peer Review: Open, with a "Community Challenges" appendix tracking critiques and replication attempts

• Key Limitation: All findings resting on simulation; field deployment and regulatory feedback actively requested

Community Engagement

• Access: Full paper, code, protocols, logs, and appendices are available at the OSF repository

• Contribute: Practitioners, regulators, and researchers are invited to test, adapt, critique, and submit new benchmarks or deployment results. All feedback and protocols updates are logged per SE Press’s living document policy.

• Contact: Paul1ESAai@gmail.com for collaboration, data access, or protocol questions.

About SE Press

SE Press provides open-source, epistemically rigorous publications and living protocols at the intersection of synthesis intelligence, ethics, and high-stakes AI deployment. All major publications are announced via press release for public and expert scrutiny; in-depth explainers are available selectively under Science Communication articles.

No companion Science Communication article is required for this release.For future challenges, test results, or partnership opportunities, SE Press welcomes your participation.