I'm a final-year Actuarial Science student with a strong academic record and experience in quantitative analysis, risk assessment and data-driven problem solving.
I have been on the Dean's List for every completed semester in Actuarial Science, received the Vice Chancellor's Award in my Diploma in Science, and currently serve as an IFoA Student Ambassador (2024–2026).
I'm seeking an internship in actuarial, analytics, finance or risk, where I can apply my technical and analytical skills in a professional environment.
02 Education
Built on a strong academic record
March 2024 – Present
Bachelor of Science (Hons.) Actuarial Science
UiTM Shah Alam, Selangor
3.67CGPA
Allcompleted semesters on the Dean's List
IFoA examination exemptions: potentially eligible for 3 exemptions.
CS2
CB1
CB2
CS2 Risk Modelling and Survival Analysis · CB1 Business Finance · CB2 Business Economics
Earlier
Diploma in Science
Vice Chancellor's Award recipient
03 Projects
Models that turn assumptions into decisions
Two projects, one in finance with Excel and one in machine learning with Python.
Keep scrolling
01
Dynamic Financial Modelling of Property Investment
Microsoft Excel
PMT
IRR
Sensitivity analysis
Developed a 15-year dynamic financial model in Microsoft Excel to evaluate residential property investment performance through monthly cash flow projections, mortgage repayments, rental income and operating expenses.
Applied Excel financial functions (PMT and IRR) to assess loan amortisation, investment profitability and long-term financial viability.
Conducted sensitivity analysis on interest rates, rental growth and maintenance costs to evaluate investment risks and support data-driven financial recommendations.
Spam Email Classification Using a Backpropagation Neural Network (BPNN)
Python
Neural network
Hyperparameter tuning
Confusion matrix
Developed a Python-based neural network to classify 4,601 emails using 57 features, incorporating data preprocessing and hyperparameter tuning.
Performed data preprocessing, feature standardisation and train-test splitting, then tuned hyperparameters to optimise model performance, the optimiser and the number of training epochs, evaluating results with precision and a confusion matrix.
Schematic only. Layer sizes are illustrative.
03.5 Try it
Two small playgrounds
Interactive, illustrative demos inspired by my work. The numbers are not my data or model parameters.
Illustrative demo · inspired by the Excel property model
Property cash-flow playground
Monthly repayment (PMT)RM 0
Net monthly cash flow, year 1RM 0
15-year equity IRR–
Total interest over the loanRM 0
Annual net cash flow after mortgage, years 1–15
Year 1RM 0Year 15
Maths: PMT = L·i ÷ (1 − (1+i)−n), with i = rate ÷ 12 and n = months. IRR solves NPV = 0 on the deposit, 15 years of net rent after costs and repayments, and sale proceeds after loan payoff.
Illustrative demo · inspired by the VoltVision pricing framework
Credibility-weighting playground
Blended premiumRM 0
Experience
Prior
Prior
Experience
Blended
Maths: Z = n ÷ (n + k), and blended premium = Z × experience + (1 − Z) × prior. More exposure (or a smaller k) means the data is trusted more.
04 Involvement
Beyond the classroom: community and competition
2024 – 2026
IFoA Student Ambassador
Institute and Faculty of Actuaries (IFoA)
Engaged with IFoA representatives, Fellows and actuarial professionals to strengthen professional networks and industry awareness.
Served as a communication link between actuarial science students and the IFoA, promoting relevant activities and opportunities while strengthening leadership and stakeholder engagement skills.
2025
Management Bureau
Actuarial Case Excellence Competition (ACEC) 2025
Coordinated an international actuarial competition involving UiTM, Malaysia and Universitas Gadjah Mada, Indonesia.
Managed programme timing and cues to ensure sessions ran smoothly and on schedule.
2026
Preliminary Round Finalist
IFoA × PIAM VoltVision Challenge 2026
Identified insurance barriers to Malaysia's EV transition using public data and actuarial assumptions.
Developed a credibility-weighted EV battery-risk pricing framework incorporating claims experience, battery condition and vehicle characteristics, while assessing its limitations and adaptability.