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.

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.

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

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

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)

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

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.

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

End-to-end insurance pricing pipeline: CatBoost frequency model, SHAP relativities, fairness audit, monitoring, and conformal intervals on a single synthetic UK motor dataset

To view the report and the semantic model of this repository click the link below

Deprecated — merged into insurance-optimise

A curated list of awesome responsible machine learning resources.

Free 12-module course: Modern Insurance Pricing with Python and Databricks. GLMs, GBMs, SHAP relativities, conformal prediction, Bayesian credibility, rate optimisation, causal demand modelling, monitoring, spatial territory rating.

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

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