Ke Xin Chong

I am an innovative Data Scientist and Machine Learning Engineer with a focus on fraud detection, risk scoring, and KYB/KYC solutions. My expertise lies in building scalable and interpretable machine learning models that leverage data-driven insights to address real-world fintech challenges. I have a proven track record of collaborating across teams to deliver impactful solutions that enhance system efficiency and stakeholder trust.

During my tenure at Advance.AI, I designed and implemented a real-time fraud detection system that significantly reduced transaction processing time. My work involved developing interpretable machine learning models using LIME and SHAP, which helped stakeholders understand feature importance and decision factors. Additionally, I have experience in developing machine learning pipelines for customer onboarding and risk assessment, ensuring a seamless integration of technology into business processes.

My previous role at Jewel Paymentech Pte Ltd allowed me to apply advanced techniques such as Variational Autoencoders for online payment fraud detection and engineer production pipelines for machine learning models. I am passionate about utilizing my skills in programming and data analysis to drive innovation in the fintech industry.