RESUME

• Implemented a robust data loading process by creating a direct link between the company’s databases and Power BI, which decreased time spent creating dashboards by 30%.

• Reduced internal fraud risk by auditing multiple teams’ operation logs by using SQL to analyze and identify risks and suggesting changes to processes.

• Designed multiple interactive dashboards using Power BI which helped better visualize different departments’ operations.

• Generated more than 20 reports using MS Office that helped management understand the results and recommendations from each audit performed.  

• Assessed more than 10 clients daily by evaluating their financial behaviour which aided the company’s compliance with the country regulations for anti-money laundering.

• Generated know-your-client reports for 30 correspondent banks by researching each bank’s anti-money laundering policies.

• Responded to 30 correspondent bank’s daily information requests by analyzing the clients’ details which helped to maintain a strong relationship with the partner bank.

• Coached more than 20 business representatives on anti-money laundering strategies on a monthly basis by motivating learning with dynamic presentations, which decreased the company’s money laundering risk.

• Developed a weekly internal newsletter with an audience reach of more than 100 employees including anti-money laundering tips which helped employees easily memorize anti-money laundering regulations.

KEY PROJECTS

• Created a machine learning model in Python to automatically extract key information from PDF and JPG receipt files using Optical Character Recognition (OCR) and Natural Language Processing (NLP).

• Performed descriptive and inferential analysis on multiple data sets using Excel and R to identify key statistics.

• Built interactive visualizations in Power BI to show the trends of COVID-19 daily cases and deaths in China compared to the country’s air quality index, feeding data from Excel.

• Performed screening, data transformations and visualizations for multiple data sets using Pandas, Numpy and Seaborn in Jupyter Notebooks.

• Performed correlation analysis to identify relationships between predictors and the response feature and build a Poisson Regression model in R to predict COVID-19 cases in USA counties.

• Built image and text classification models with Logistic Regression, Random Forests, and Neural Networks using Python.

• Wrote insightful reports using MS Office tools that effectively provided informed findings and insights from the analysis performed.