Machine learning for financial risk management

A framework for estimating Basel IV capital requirements.

A systems-thinking essay arguing that most optimization quietly trades away buffers, slack, and resilience to make present metrics look better. It reframes efficiency as borrowing stability from the future, and shows how education, workforce, infrastructure, markets, and hardware all get optimized into fragility.

The repo contains the main topics carried out in my master's thesis on operational risk. In particular, it is described how to implement the so called Loss Distribution Approach (LDA), which is considered the state-of-the-art method to compute capital charge among large banks.

Risk-based SLA compliance and remediation pressure monitoring framework built in Power BI with custom prioritization logic.

Streamlit dashboard tracking vendor contract renewal risk — flags 30/60/90-day expiry windows, scores SLA + data issue risk, tags root causes, and exports Excel action reports.

Business Continuity Plan and organizational Risk Profile for the simulated AtlasPay environment. Includes critical process analysis, recovery priorities, impact assessment, and resilience strategies aligned with governance and operational risk best practices.

Operational risk Monte Carlo (Poisson/Lognormal) for collision losses—methods, R code, and 99.9% capital estimate.

Analytical portfolio demonstrating transaction monitoring, judgment-based alert review, and Excel-driven risk analysis across fraud, AML, and KYC workflows, with a focus on regulator-safe decisioning and operational consistency.

A quantitative framework for modeling Operational Risk Capital under Basel III standards using the Loss Distribution Approach (LDA). Implements Monte Carlo convolution of Poisson frequency and Generalized Pareto (Heavy-Tailed) severity distributions to calculate the 99.9% Value at Risk (VaR).

End-to-End data engineering pipeline (Python/PostgreSQL) and interactive Power BI dashboard designed to identify compliance risks and automate financial anomaly detection.

🧪 Laboratorio de sistemas distribuido con 12 casos reales Docker-first para diagnosticar y resolver problemas críticos de rendimiento, observabilidad, resiliencia y arquitectura 🐳

AI That Finds What's Quietly Killing Your Business

Strategic ROI Framework for SecOps Governance & Automation for Infrastructure Governance & Workflow Automation. Optimized for Healthcare (HIPAA-aligned) and Enterprise operations. Built to quantify "Manual Leakage" and operational risk

Proof-of-concept AI fraud intelligence pipeline built on GCP and Vertex AI — multi-agent system for surfacing emerging fraud risks from unstructured news. Confidential project, public summary available.

⚖️ Explore how optimizing systems can borrow stability from the future, emphasizing resilience and balance over short-term gains.

Build AI-driven crypto trading infrastructure with compliance-first tools for trusted digital finance

LDA probabilistic risk profiling — latent risk archetypes, portfolio mix drift, book-transfer segmentation

Monitor and prioritize operational risk by tracking SLA compliance, backlog growth, and escalation to detect issues before impact occurs.

🔍 Analyze transaction data to identify fraud risks, streamline alert reviews, and ensure compliance in AML and KYC contexts using Excel-driven techniques.