Daryl-Tawana/ClaimSense

An AI-powered insurance claims intelligence and risk-modelling platform. # ClaimSense > Don't just consume knowledge. Produce evidence. ClaimSense is an open-source learning and research project exploring the intersection of: - Actuarial Science - Statistics - Data Science - Machine Learning - Artificial Intelligence - Risk Modelling - Software Engineering ## Project Objective The goal is to investigate how actuarial models, statistical methods, machine learning and AI can be combined to improve insurance claims intelligence and risk assessment. This project is being developed as a long-term learning laboratory by a second-year Actuarial Science student in Zimbabwe at the Midlands State University ## Research Questions Some of the questions we intend to investigate include: 1. How can insurance claim frequency be modelled? 2. How can claim severity be modelled? 3. How can aggregate losses be estimated? 4. How can Monte Carlo simulation be used for portfolio risk? 5. Can machine learning improve claims prediction? 6. Can anomaly detection identify unusual claims? 7. How can AI and NLP extract useful information from claims descriptions? 8. Can explainable AI make complex models useful to actuarial decision-makers? 9. How sensitive are our models to their assumptions? ## Project Philosophy We are not simply building software. We are documenting the process of learning, experimenting, failing, validating and improving. Every major stage should produce evidence: - mathematical reasoning - experiments - code - visualisations - models - research findings - documentation ## Status 🚧 Project initiated — August 2026 ## License This project is intended as an educational and research project.