Balisa50/life-insurance-risk

Actuarial risk model for Sub-Saharan African life insurance. Gompertz-Makeham mortality, Kaplan-Meier survival, Cox PH, actuarial pricing, Monte Carlo VaR. # Life Insurance Risk Model Actuarial risk modelling for Sub-Saharan Africa. Built as part of learning actuarial science - wanted to implement proper mortality models from scratch rather than use a black-box package. ## Models **Mortality**: Gompertz-Makeham. Fits the exponential mortality increase with age plus a constant background hazard rate. Parameters estimated on 5,000 synthetic Sub-Saharan Africa profiles calibrated to regional age distributions. **Survival analysis**: Kaplan-Meier curves with log-rank tests across risk groups. Cox Proportional Hazards for covariate effects - C-index of 0.77 on holdout. **Premium pricing**: Actuarial present value framework. Premiums back-calculated from mortality tables with loading factors. **Stress testing**: Monte Carlo simulation across 5,000 scenarios including pandemic shock (mortality multiplier 1.8x–3.5x, calibrated loosely to COVID mortality data). VaR at 95th and 99th percentile. ## Stack - Python - lifelines, NumPy, pandas, scipy - Next.js + Recharts - interactive dashboard ## Running ```bash # Modelling pip install -r requirements.txt jupyter notebook notebooks/ # Dashboard cd dashboard npm install && npm run dev ``` ## Live life-insurance-ab.vercel.app