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Senior Consultant in Financial Risk Management at EY, building intelligence layers for modern finance.
I'm an Senior Consultant in Financial Risk Management at Ernst & Young (EY) Hong Kong, where I apply cutting-edge artificial intelligence and machine learning algorithms to help financial institutions navigate an increasingly complex regulatory and risk landscape.
Malaysian at heart, data-driven by passion—leveraging AI, econometrics, and advanced analytics to reshape financial risk and decision-making in a fast-evolving world.
Alumnus of MCKL, where I achieved straight A* in CIE A-Levels (Further Mathematics, Mathematics, Physics, Chemistry). Initially gearing up for mechanical engineering, I pivoted toward data science and econometrics, drawn by their power to uncover insights from complex systems. This foundation led to a BSc in Econometrics and Data Science at the University of Amsterdam, followed by an MSc in Financial Technology from HKUST.
At EY, I drive innovation in financial risk management, specializing in behavioral models for interest rate risk in the banking book (IRRBB), early redemption risks, and predictive accuracy.
I harness Python automation, R, machine learning, and Retrieval-Augmented Generation (RAG) with large language models to streamline workflows, boost model performance, and deliver explainable, regulatory-compliant solutions.
Outside of work, I have a genuine passion for aviation — commercial airliners, airport operations, and the elegant complexity of flight networks and logistics. It’s a hobby that constantly reminds me of the beauty in high-dimensional systems and precise optimization.
Open to conversations on AI in finance, behavioral modeling, LLMs/RAG in risk, econometrics, or just sharing a solid data pun. Let’s connect!
Senior roles in financial risk, model validation, and AI-driven solutions at leading institutions.
Specialised in advanced machine learning and application to various domains such as financial forecasting. Covering all topics such as deep learning, graph neural networks, algorithmic trading, blockchain.
Rigorous quantitative training combining econometrics, causal inference, time-series analysis, and modern data science methods
Foundational AI curriculum covering symbolic AI, Logic, Algorithms and Data Structures, Programing in C
Achieved straight A* in Further Mathematics, Mathematics, Physics, and Chemistry. Initially focused on mechanical engineering but developed a strong passion for data science and AI, leading to a pivot in academic focus.
Small tools I actually use. More will land here as I ship them.
Drop a QR screenshot, paste the LPA string, or type the SM-DP+ fields. I build a link you open on the phone that needs the plan — iPhone or Android does the rest. Nothing is uploaded.
The pairwise interpretation of ROC AUC — 'probability a random positive outranks a random negative' — isn't just a nice intuition pump that happens to give the same number as the curve. It's mathematically the same computation. Here's why, worked through an 8-point example both ways.
Fraud, default, churn, cancer — the outcome you actually care about is almost always the rare one. SMOTE and class weights fix the training problem, but they quietly break your predicted probabilities unless you correct the intercept back afterward. Here's the fix, from King & Zeng (2001).
Wilson confidence intervals are a great governance tool for monitoring behavioural assumptions — but at large sample sizes they get so tight that trivial, immaterial drift starts failing the check. Cohen's h fixes that by measuring the size of the gap, not just whether it's detectable.

Investment Opportunities in the Gaming and eSports Industry
Hong Kong University of Science & Technology

Bachelor Thesis
University of Amsterdam

Assignment: High Bias in Labour Market
University of Amsterdam

Empirical Project
University of Amsterdam

Colour in Visual Cognition
University of Groningen
I’m always open to discussing interesting AI & finance problems, consulting engagements, research collaborations, or just connecting with fellow fintech enthusiasts.
Drop me a message and I’ll get back to you within 48 hours.