Actuarial Analytics

Proficiency Level: 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

  • Statistical Modeling: Experience using R and Python for academic and personal data analysis projects
  • Actuarial Models: Familiar with basic actuarial models used in pricing, reserving, and risk management
  • Statistical Methods: Exposure to regression analysis, time series modeling, and hypothesis testing in coursework
  • Data Visualization: Able to create visualizations using ggplot2 (R) and Seaborn/Matplotlib (Python)
  • Risk Management: Understanding of fundamental risk principles and insurance frameworks
  • Financial Analysis: Practical understanding of financial mathematics and modeling concepts
  • Process Automation: Introductory experience using Python and VBA to automate small-scale tasks
  • Excel: Competent with formulas, pivot tables, and basic macros
  • Database Management: Comfortable working with SQL for querying small datasets
  • Communication: Able to explain technical findings in a clear, structured way

Academic Projects and Practice Work

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 and Skills

Programming Languages:

  • R: Data analysis, model fitting, visualizations
  • Python: Data manipulation, basic ML models, automation
  • SQL: Writing simple queries for data extraction
  • VBA: Automating Excel tasks

Software and Tools:

  • Excel: Data modeling and presentation
  • Tableau/Power BI: Early-stage data visualization skills
  • Prophet: Used for basic time series forecasting in Python

Continuous Learning

I’m actively building my skills by:

  • Preparing for actuarial exams
  • Exploring actuarial use-cases in machine learning and analytics
  • Reading actuarial journals and industry updates
  • Engaging in projects that bridge academic theory and practical application

I'm enthusiastic about transitioning from student to practitioner and continuously improving my ability to contribute meaningfully in actuarial and data-driven roles.