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.

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.

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

Data visualization about smoking impact on insurance annual charges

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

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

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.

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

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

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.

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

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

Density ratio correction for insurance pricing book shifts — CatBoost/RuLSIF/KLIEP, LR-QR conformal, FCA SUP 15.3 diagnostics

Constrained rate optimisation for insurance pricing — FCA ENBP compliance, demand modelling, efficient frontier, portfolio-level margin control