- Built search tool in Excel that provides company information and calculates adjusted emission intensity for 6 sectors and allows traders to monitor emission performance against DBS bank’s annual target - Performed risk analysis of DBS non-retail borrowers’ assets for CN and TW markets for annual sustainability report that discloses bank’s overall risk level under climate change scenarios- Using VBA and Python, mapped 240K+ DBS collaterals of the entire portfolio with latitude and longitude and floor numbers; Analyzed and visualized all collaterals on an interactive map using Folium for MAS Industry-Wide Stress Test- Obtained physical risk scores of 8 hazard types to DBS collaterals by assigning proxy assets using external dataset based on nearest distance using Python- Insured data quality by cross validating data from different vendors and internal data

- Pulled historical payment data for users using SQL and analyzed the optimal threshold values for ~8 fraud defense rules that minimize costs using Python for 10+ markets- Applied SQL and PivotTable, explored and proposed 3 rules that can be used to block suspicious transactions, which is estimated to block ~20% of the chargeback volume with 47% accuracy- Discovered common behaviors through analyzing fraudulent activities, proposed defense rules for anomaly detection with 95%+ accuracy and 35%+ coverage; Collaborated with security team on indicator building and rule implementation- Collaborated with data science team to validate internal defense model; A/B tested on the checkout funnel and built simulator to advise model thresholds; Maintained similar chargeback and approval rates without additional defense layer- Constructed workflows with SQL to pull out users’ payment attempt data and built Google Sheets to monitor chargebacks received and performance of different defense layers. - Automated data extraction from Google Sheets and sends summary table to stakeholders daily using Python

- Utilized SQL to extract client features and analyzed how different variables would affect a lead’s likelihood of becoming a client; identified variables with higher significance from internal data using bivariate analysis- Studied the attributes of accredited investors (high value investor), identified the most important variable for accredited investors, and provided the information to help RMs improve communication techniques accordingly- Enhanced DBS brand in a sustainable campaign initiated by the Singaporean government; utilized SQL to process over 10 million rows of data to provide suggestions on how to improve the coefficients used to calculate carbon emission- Utilized Selenium to download persona-related data and calculate results, visualized the results and sent them to clients