About

Apoorv Agrawal is a highly skilled Director in Fraud Risk Decision Science with over 6 years of relevant experience. He is currently working with American Express as a Director in Fraud Risk Decision Science. Apoorv has an impressive educational background, having completed various Post Graduate Diplomas in Business Analytics, Machine Learning, and Statistics from prestigious institutions such as IIM Calcutta, IIT Kharagpur, and Indian Statistical Institute, Kolkata. He also holds an Engineer's Degree in Mechanical Engineering from NIT Surathkal. Apoorv's professional journey began with American Express, where he started as a Manager in Fraud Risk Machine Learning and later became a Senior Manager in Fraud Risk Decision Science. He has worked on various projects related to fraud detection, risk management, and machine learning. Apoorv is proficient in using ML as a tech stack and has a deep understanding of statistical modeling and data analysis. As a Director in Fraud Risk Decision Science, Apoorv is responsible for developing and implementing strategies to detect and prevent fraud in American Express. He leads a team of analysts and data scientists to develop models, algorithms, and tools to mitigate fraud risk. Apoorv is also responsible for collaborating with cross-functional teams to ensure the smooth functioning of fraud risk management processes. Apoorv is a highly motivated individual with excellent problem-solving skills. He is passionate about using data-driven approaches to solve complex business problems. Apoorv is a team player and has excellent leadership skills, which he uses to motivate his team and achieve organizational goals. He is a quick learner and keeps himself updated with the latest industry trends and developments. Overall, Apoorv Agrawal is a highly skilled and experienced Director in Fraud Risk Decision Science, with a strong educational background and a deep understanding of ML and statistical modeling. He is a valuable asset to any organization and is committed to achieving organizational goals by using data-driven approaches.