Repository of GEMAct source code. Enjoy!

GLM, Neural Network and Gradient Boosting for Insurance Pricing, Part 1: Claim Frequency

XGBoost Regressor to predict healthcare expenses based on features such as age, BMI, smoking, etc.

Full-stack CRM for insurance operations with lead management, customer tracking, policy workflows, and a responsive dashboard UI.

Claw🦞 agentic driven InsurTech🛡🤖AI-driven infrastructure that programmatically underwrites, covers risk, and processes claims/fraud with precise, algorithmic protection.

GLM tooling for insurance pricing — nested GLM embeddings, R2VF factor level clustering, territory banding, SKATER

InsureSight is an intelligent insurance pricing engine that leverages ML to forecast premiums using demographics, medical history, and lifestyle factors. Delivers instant, data-driven cost predictions via an intuitive Streamlit interface.

Data visualization about smoking impact on insurance annual charges

Constrained portfolio rate optimisation for insurance pricing — SLSQP, FCA ENBP, efficient frontier, shadow prices, JSON audit trail

Telecom outage detection and parametric insurance framework covering customer segmentation, pricing, basis risk and Monte Carlo capital modelling.

Model governance for insurance pricing — PRA SS1/23 validation reports, model risk management, risk tier scoring

Unofficial Node.js SDK tailored for MyCover.ai.

This is the backend server for AssuredLife - a modern life insurance management platform. It is a role-based full-stack web application built with the MERN stack. It provides a secure and robust REST API to support the client-side application.

Actuarial tail risk quantile/expectile regression for insurance pricing - TVaR, large loss loading, ILF curves, CatBoost

AssuredLife is a modern, full-stack web application designed to streamline the process of purchasing and managing life insurance policies. It provides a secure, responsive, and role-based platform for customers, agents, and administrators.

Insurance Premium Optimization (End-to-end ML Ops project)

The complete actuarial pricing flow for Python. A structured framework that unifies GLM modeling, proper offset handling, and predictive diagnostics for professional non-life insurance pricing.

GAMLSS for insurance pricing in Python — model variance, shape, and tail parameters as functions of covariates

Model drift detection for insurance pricing — exposure-weighted PSI/CSI, A/E ratios, Gini drift z-test