Data Collection and Analysis
- Utilizing IPython Notebook, earthquake data from the USGS is collected, specifically focusing on a specific geographical location. This dataset serves as the foundation for designing an Insurance Linked Security (ILS) to be launched in the capital markets.
- Pandas is employed for time series analysis of the historical earthquake data, enabling in-depth exploration and insights.
Monte Carlo Simulations and Exceedance Loss Probabilities
- Monte Carlo Error Propagation technique is applied to generate simulated earthquakes, hurricanes, and other relevant events. These simulations aid in calculating exceedance loss probabilities over the life of the ILS, providing valuable risk assessment.
- By extending the capabilities of Monte Carlo simulations, this approach enables comprehensive analysis and forecasting of potential losses associated with the ILS.
Custom Machine Learning for Optimal Parameters
- Custom ML code is developed to select the best parameters for fitting the target credit rating, ILS parameters, and predicting the spread over LIBOR of the security. This integration of machine learning techniques enhances the accuracy and precision of the model.
- Classification and regression models, implemented using pandas, MC simulations, and other relevant tools, make this hybrid quant/ml model well-suited for analyzing various insurance risks, particularly tail risks, utilizing publicly available data.
Enhanced Risk Analysis and Geographical Coverage
- To further enhance risk analysis, vectorization of categorical features could be employed. This technique has the potential to improve loss ratios, surpassing the performance of Generalized Linear Models (GLMs) which do not account for the non linearity of interacting features.
- Geographical coverage is visualized using D3JS, allowing for a comprehensive understanding of the spatial aspects of the risks being evaluated, and quick graphical represenation of concentration of insurance risks.
Business Application
- This framework, incorporating fixed income analysis techniques and machine learning, holds immense potential for pricing and managing various insurance risks. It effectively assesses risks characterized by low frequency and high severity using techniques like Monte Carlo simulations and enhanced risk analysis.
- The versatility of the framework extends beyond earthquake insurance, allowing for the pricing and management of other risks such as hurricanes, floods, or rare events like asteroid impacts, but also more mundane risks such a many lines of Property & Casualty Insurance and some types of Life Insurance.
- It is crucial to consider risk distribution in building a successful insurance venture. While advanced modeling techniques provide valuable insights and scalable pricing capabilities, a robust and sustainable insurance business requires the ability to distribute risk across a diverse pool of insured entities.
- Before venturing into your own fintech venture, ensure you have a well-rounded plan that encompasses advanced risk assessment techniques and partners/colleagues that know the critical aspect of risk distribution, fundamental to any insurance business.
Good luck taking over the world of insurance! 😄 🌎 🎉
2014 YouTube Demo, articles, etc.