Quantitative Modeling and Data Analysis• Modeled and validated updates to dynamic economic assumptions (Mortality, Lapse, Expenses, Portfolio Rate, Capital factors) for (Variable) Universal Life products, troubleshooted coding corrections in Prophet software• Developed an automated solution framework in Excel/ VBA to calculate a $700k refund (including monthly interest) for policyholders affected by data discrepancies in M&E charges and loan interest• Streamlined singular high-value policy pricing processes across 9 Retail Life products by updating and validating Prophet model (variable and functionality update), and presenting the new working models to Model Development TeamFinancial Reporting and Forecasting• Analyzed financial impacts of assumption updates by conducting annual Non-Guaranteed Element (NGE) and Disciplined Current Scale (DCS) testing across all Retail Life products• Performed quarterly Value of New Business analysis on (V)UL products, querying model results to income statements and investigated the profitability/ loss of assumption/ model updates on a roll-forward basis• Conducted an experience study on 6 Future mortality improvement (FMI) rate scenarios for (V)UL products, assessing key metrics namely 30-yr & 99-yr Marginal Value Added (MVA) and Internal Rate of return (IRR)Project Management and Communication• Led monthly crediting rates monitoring processes, ensured accuracy and quality of deliverables, finalized and presented rate change recommendations to executive management committee via Workiva deck• Tested reinsurance allocation scenarios (quota share, rates, treaties) for large policy issuances (face amount > $1M), delivering profitability analyses in 24-hour turnover to support strategic decisions• Managed ad-hoc actuarial analyses in collaboration with Solutions, Product, Technology teams, estimating a ~$35M profit for sale campaigns using sales data (face amount, underwriting/ mortality cost, product level & lapse factors)

• Performed data processing (collection, cleaning, and reconciliation) to conduct an experience study on Product Lapse Rates based on policy count and face amount from 2009 to 2021 utilizing Excel and automation via VBA• Analyzed results in lapse study using actual-to-expected ratios and proposed a new assumption for lapse rates by writing a memorandum for the study and presenting to the Experience Study Working Group

• Projected macro population and economic parameters to build the main data frame for the Valuation Model for the Vietnam Social Insurance Fund• Developed the model to evaluate the financial sustainability of the Social Insurance Reserve Fund of Vietnam and simulate different scenarios with population, economic, and fund investment input parameters• Utilized ILO materials focused on actuarial valuation methods for national security schemes and literature review to address possible policy changes to improve the Social Insurance Reserve Fund performance

• Built Tableau dashboards on the Applied Analytics Department’s most active projects and their progress/ outcomes• Developed a machine learning model to predict the final cancel indicator of insurance policies with the Customer Life Value Dataset by using Classification ensemble methods, which resulted in an increase in AUC score • Created a pipeline model that queries the Customer Life Value Dataset with input variables (policy type, time interval, and evaluation date) utilizing SQL to set up and process the data frame, perform EDA, fit, and tune Supervised Learning Models, and return diagnostics as well as performance metrics for comparison of accuracy and model efficiency