Data Analyst
Current•Developed machine learning models using neural autoencoders and established a dynamic threshold-based anomaly detection system, enhancing fraud detection rates by 15% and reducing false positives by 10%.•Led the data cleaning, preprocessing, and transformation, optimizing SQL queries and extraction workflows with AWS Athena and AWS Redshift to improve process efficiency by 20%, ensuring quick and reliable data for fraud detection model development•Implemented SMOTE to balance data, enhancing model sensitivity towards fraudulent transactions by 25%, improving detection accuracy by optimizing minority class representation.•Reviews, verifies, and/or identifies identity theft to detect/ prevent financial crimes activities, policy violations, and suspicious situations in order to mitigate and/or recover losses.•Utilized Google Analytics and Google Tag Manager to refine KPIs such as click-through rate, open rate, and bounce rate, enhancing targeted ad campaigns. Achieved over 26% improvement in conversion rates by accurately engaging the correct customer segments through Blueconic CDP.•Extracted and analyzed customer data from financial databases, CRM systems, and online platforms to identify churn factors, visualized key metrics using interactive Tableau dashboards, and engineered an ETL pipeline from Blueconic CDP to Salesforce CRM, streamlining data delivery for actionable insights to sales teams•Collaborated with cross-functional teams to define critical KPIs for the Credit Card Fraud Detection dashboard, including Fraud Detection Rate and False Positive Rate, enhancing data-driven decision-making and operational effectiveness.•Create rules to stop the fraud account opening by identifying the patterns thru Data analysis and Data Science techniques and monitoring the trends and suspicious accounts and taking necessary action in case of a sudden spike.
