Tools for creating and working with aggregate probability distributions.

An open-source Python framework for actuarial cash flow models

A Python package for survival analysis. The most flexible survival analysis package available. SurPyval can work with arbitrary combinations of observed, censored, and truncated data. SurPyval can also fit distributions with 'offsets' with ease, for example the three parameter Weibull distribution.

Python package to solve actuarial life-contingent risks

Classification of reserve risk with chain-ladder

This holds all my personal data-related project's (Automation, Modelling, Analysis)

Functions for finding premium in several ways, calculating the reserve in an equivalence principle, and calculating the reserve in an Euler differential equation

Pure-Python library of heavy-tailed probability distributions (Pareto, Burr, LogNormal, etc.) built from first principles.

CATIA is a catastrophe AI system that integrates advanced artificial intelligence, actuarial science, risk analysis, and machine learning to provide robust assessments of natural hazards such as hurricanes, floods, and wildfires, with a focus on financial impacts and mitigation strategies

A framework for Actuarial Modelling

Actuarial data science project analyzing insurance claims to identify potential fraud and assess customer risk. Includes Python preprocessing, feature engineering, and a stakeholder-ready Tableau dashboard.

A repository that is filled with quantitative finance projects which aim to cover the rigorous mathematics involved as well as providing interactive simulations of the respective models.

Research project 2019-2021

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).

Python-based Financial Asset Manager with Monte Carlo Risk Simulation.

Research project 2020-2021

Aplicação desenvolvida como resultado do Trabalho de Conclusão do Curso em Ciências Atuariais pela Universidade Federal de Pernambuco. Seu objetivo consiste no cálculo de Provisões Matemáticas em planos de Benefício Definido para Entidades Fechadas de Previdência Complementar.

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

This library migrates the pandas feature and prophet table requirements

Monte Carlo simulation of insurance losses using a frequency–severity model with Poisson claim frequency, lognormal and gamma severity distributions, and tail risk analysis.