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.

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

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).

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.

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.

📊 Model operational risk capital using the Loss Distribution Approach (LDA) and Monte Carlo methods for accurate economic risk assessment.

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