Lu Cheng

Spearheaded the development of customer analytics and commercial rating models with End-to-End Modeling Leadership. Designed and implemented the bank’s first-generation first-party fraud detection models for Caribbean credit card and unsecured loan portfolios, achieving 10x to 50x lift in fraud identification. Redeveloped multiple credit rating models for business borrowers, improving performance

Developed first-generation advanced attrition and points redemption models for credit cards, leveraging discrete event-history modeling and feature engineering. Developed Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD) models, ensuring regulatory compliance (IFRS 9). Validated derivative pricing models and risk rating models for asset-backed securities, replica

Validated multiple retail product stress testing models, including PD, LGD, EAD, PPNR, and fair lending. Led the validation of a pioneering Credit Card PD model, which successfully met all IFRS9, stress testing, and Advanced Internal Rating-Based requirements. Contributed to the development of model monitoring frameworks, designing metrics and establishing thresholds to evaluate model ongoing perf

Applied a simple yet efficient dimensionality reduction algorithm to perform feature selection on data with extremely low signal-to-noise ratios, integrating genetic pathway information. Contributed to oral and poster presentations, and a published paper at the Genetic Analysis Workshop 17. Built several diagnostic and predictive time-to-event survival analysis models on head and neck cancer patie

Applied cluster-effect-adjusted non-parametric tests and longitudinal data modeling to identify risk factors affecting new-born babies. Performed time series analysis and built multiple regression models to explain clinical outcomes for various medical research projects.

Grade: Top 1 in PhD Stage I Comprehensive Examination

Awarded 'Top 1' in the PhD Stage I Comprehensive Examination, demonstrating strong foundational knowledge in core statistical courses. Conducted thesis research on large-scale complex feature selection and inference.

Completed a Master of Statistics degree with a co-op component, gaining practical experience in the field. Focused on advanced statistical methodologies and their applications.

Obtained a Bachelor of Science degree in Statistics, building a strong foundation in statistical principles and methods. Developed analytical skills essential for data-driven problem-solving.