Risk Modelling and Survival Analysis Core Principles

Name: Risk Modelling and Survival Analysis Core Principles
Rating: 3.7 (26 reviews)

Mastering Quantitative Techniques for Assessing Risk and Predicting Survival

Created bySourav Das

Last updated 2/2024

English

What you'll learn

Understanding the concept of risk, its types, and the role of actuaries in managing risk in various industries such as insurance, finance, and healthcare.
Introduction to survival analysis techniques, including Kaplan-Meier estimator, hazard functions, censoring, and truncation.
Exploring probability distributions commonly used in survival analysis, such as exponential, Weibull, and log-normal distributions.
Understanding parametric models like the exponential, Weibull, and log-logistic models.
Techniques for handling censored data in survival analysis, including right-censoring.

Course content

1 section • 8 lectures • 31m total length

Introduction1:01
Overview of risk management and its importance in various industries.
Role of actuaries in assessing and mitigating risk.
Introduction to survival analysis and its application in actuarial science.
Brief overview of the topics covered in the course.
Loss Distribution Risk Sharing3:39
Understanding the concept of loss distribution and its implications.
Methods of risk sharing, including reinsurance and alternative risk transfer mechanisms.
Analysis of risk pooling and its benefits in spreading risk across a larger population.
Case studies illustrating the effectiveness of different risk sharing strategies.
Properties of Statistical Distribution1:48
Definition and properties of statistical distributions commonly used in actuarial science, such as normal, exponential, and Weibull distributions.
Understanding parameters, moments, and cumulative distribution functions.
Importance of distributional properties in risk modeling and survival analysis.
Loss reinsurance, deductibles5:05
Explanation of reinsurance and its role in transferring risk from primary insurers to reinsurers.
Types of reinsurance contracts, including quota share, excess of loss, and stop-loss.
Introduction to deductibles and their impact on risk retention and risk transfer.
Calculation and analysis of reinsurance premiums.
Derivations of distributions, Maximum likelihood and methods of moments4:50
Methods for assessing deviations of observed data from theoretical distributions.
Introduction to maximum likelihood estimation and its application in parameter estimation.
Explanation of the method of moments and its use in estimating distribution parameters.
Comparison of maximum likelihood and method of moments approaches.
Risk Modelling5:31
Techniques for quantifying and modeling various types of risk, including financial risk and mortality risk.
Overview of risk assessment methodologies, including scenario analysis and stress testing.
Application of risk models in insurance, finance, and other industries.
Evaluation of model accuracy and reliability.
Compound Poisson Distribution3:50
Definition and properties of the compound Poisson distribution.
Application of compound Poisson distribution in modeling aggregate claims in insurance.
Calculation of moments, probabilities, and percentiles for compound Poisson distributions.
Interpretation of results and implications for risk management.
Introuction to Copulas6:00
Overview of copulas and their role in modeling dependence between random variables.
Explanation of copula functions and their properties.
Application of copulas in risk modeling, particularly in assessing dependencies between different risks.
Interpretation of copula-based results and practical considerations for their implementation.

Requirements

No basic prerequisite

Description

Embark on a transformative journey into the heart of actuarial science with comprehensive course on Risk Modelling and Survival Analysis Core Principles. Dive deep into the sophisticated realm of quantitative techniques and statistical methodologies that underpin the assessment of risk and the prediction of survival.

In this meticulously crafted course, you'll unravel the intricacies of survival analysis, equipping yourself with powerful tools to decipher complex data patterns and extract invaluable insights. From mastering the estimator to delving into advanced parametric and non-parametric survival models, you'll gain the expertise needed to navigate real-world scenarios with confidence and precision.

But this course isn't just about theoretical concepts; it's about practical application. Through hands-on exercises and immersive case studies, you'll apply your newfound knowledge to solve challenging problems encountered in insurance, finance, and beyond.

By the end of this course, you'll not only possess a deep understanding of risk modelling and survival analysis principles but also the practical skills to excel in a competitive marketplace.

Key Highlights:

- Explore fundamental principles of survival analysis and risk modelling

- Master parametric and non-parametric survival models

- Apply statistical techniques to real-world actuarial problems

- Gain hands-on experience with industry-standard software tools

- Stay ahead of the curve with cutting-edge insights and emerging trends

Who this course is for:

Provide essential knowledge and skills required for actuarial roles, including risk assessment, pricing.
Established actuaries looking to enhance their skill set or stay updated with industry best practices
Professionals working in risk management or quantitative analysis roles can gain valuable insights from this course.
Individuals working in the insurance industry, such as underwriters, product managers, and claims analysts
Graduate students pursuing degrees in fields like statistics, mathematics, economics, or finance can find this course beneficial for their academic and career development. It offers a specialized focus on survival analysis and risk modeling