At AML RightSource, I investigate AML, fraud, and transaction-monitoring alerts in a regulated fintech environment. I review suspicious activity and high-risk customer accounts, assess transaction patterns, and document case decisions.
I review 25+ cases daily while meeting SLA requirements and maintaining an average quality score above 95%. I identify red flags and escalate cases for enhanced due diligence or regulatory review when needed.
At National Institute of Technology Mizoram, I developed an IoT device using Arduino and sensors to collect field data. I also created a server to store the data and display it on a website, and contributed to an ensemble machine learning model with 87% accuracy.
As a Data Analyst Intern at Rubixe, I contributed to data-driven analysis and used SQL to extract and analyze data. I also built Power BI dashboards for projects examining suspicious transaction trends, fraud-risk indicators, and customer segmentation.
