● Applied Machine Learning to predict health risks for middle-aged patients to inform a new insurance product line. Three hypertension risk prediction models were built using logistic regression, random forest and support vector machine algorithms, resulting in a prediction accuracy of 87% for one of the models built based on support vector machines, improving the prediction of health risks for customers.● Created data visualisations in R and wrote periodic reports for clients on market trends across fintech, education and medicine.● Developed proficiency in TensorFlow, ROCR tools, Numpy, ggplot and various other software packages to apply into research on resource allocation in healthcare organisations.

● Built a consumer sentiment analysis model using Python and Power BI for BMW, which enabled BMW to propose a product strategy for quarterly model adjustments.● Used R and VBA to estimate the shadow price of intangibles within the medical industry to avoid significant deviation of fund NAVs from market NAVs using amortised cost methodology.● Conducted financial due diligence checks for two IPOs, in the medical and property sectors, further evaluating the historical financial position of the companies and the validity of reported figures in terms of inventory utilisation and turnover rates.● Ensured compliance between KPMG’s internal processes and Chinese accounting standards, and contributed to a more reasonable proposal and design of computerised transaction vouchers for the core transaction system design of MUFG.● Completed an ESG certificate in Quantitative Finance, issued by Cambridge University & KPMG, which led to preliminary research on the fair estimation of carbon emissions trading values for corporate clients.

● Applied Python language to clean and penetrate the market data of fintech companies' products, increasing the filtering efficiency of the operation system.● Processed risk assessment of financial products issued by listed companies, effectively lowering the price volatility rate.● Participated in strategy development for different regional markets through 4P product modelling, enhancing regional market penetration rate by 23%.

● Performed ETF arbitrage strategy backtesting, using JQdata to extract real-time quotes, improving quarterly returns by 3%.● Completed research reports on water futures and a time series analysis of crude oil futures time series analysis report, which resulted in a sound quote presentation and investment bid for a hybrid green financial product.● Be responsible for data cleaning via Power BI and generating heuristic proxies for missing data, reducing system data omissions rate by 2.3%.● Automated risk classification via Python, based on mean, variance and historical sector risk, mitigating a 1.12% risk threshold in moving average volatility.

● Applied behavioural analysis models to quantity market sentiment for client products and their competitors, thus effectively reducing the expected risks of market products.● Created real-time data visualisation models to capture trends in competitor products.● Conducted quality control checks on data, and cleaned data for improved searchability, doubling the searching efficiency for the project.