Actuarial Analytics
Intermediate
I'm a final-year student pursuing a Bachelor of Commerce in Actuarial Science. Through coursework, academic projects, and hands-on learning, I've gained solid exposure to core actuarial tasks and tools. My current focus is on developing practical skills in modeling, data analysis, and regulatory compliance.
Core Competencies
Experience using R and Python for academic and personal data analysis projects
Familiar with basic actuarial models used in pricing, reserving, and risk management
Exposure to regression analysis, time series modeling, and hypothesis testing in coursework
Able to create visualizations using ggplot2 (R) and Seaborn/Matplotlib (Python)
Understanding of fundamental risk principles and insurance frameworks
Practical understanding of financial mathematics and modeling concepts
Introductory experience using Python and VBA to automate small-scale tasks
Competent with formulas, pivot tables, and basic macros
Comfortable working with SQL for querying small datasets
Able to explain technical findings in a clear, structured way
Academic Projects
IFRS 17 Reporting Practice
- →Worked on mock IFRS 17 reporting frameworks during coursework
- →Studied approaches to liability measurement and transition requirements
Pricing and Reserving Models
- →Built simple pricing models in R and Python as part of class projects
- →Used chain ladder method on small datasets for mock reserving exercises
Data Analytics Projects
- →Created scripts in Python and R to clean, explore, and visualize insurance data
- →Built simple dashboards in Excel and explored Tableau/Power BI basics
Regulatory Frameworks
- →Studied local and international compliance standards (e.g. Solvency II, IFRS 17)
- →Learned how actuarial reports are prepared for regulatory purposes
Technical Tools
Programming Languages
Software & Tools
Continuous Learning
I'm enthusiastic about transitioning from student to practitioner and continuously improving my ability to contribute meaningfully in actuarial and data-driven roles.