Andria Sarri
About Andria Sarri
Andria Sarri is a Data Scientist with extensive experience in fraud detection and prevention within financial institutions. She has worked at HSBC since 2015, contributing to the development of machine learning models for identifying fraudulent activities.
Work at HSBC
Andria Sarri has been employed at HSBC as a Data Scientist since 2015. In this role, she has focused on developing machine learning models aimed at identifying fraudulent activities. Her work contributes to the bank's efforts in fraud detection and prevention, which are critical for maintaining security and trust in financial transactions. Additionally, she has played a significant role in the HSBC Financial Intelligence Unit, enhancing data-driven decision-making processes within the organization.
Education and Expertise
Andria Sarri holds a Doctorate in Philosophy, which she earned from University College London between 2010 and 2014. Her academic background also includes a Master's degree in Applied Statistics from the University of Oxford, completed in 2010, and a Bachelor's degree in Mathematics from the same institution, which she obtained in 2009. This educational foundation supports her expertise in data science, particularly in the areas of applied statistics and machine learning.
Background
Before joining HSBC, Andria Sarri gained experience in academic research. She worked as a Research Consultant at UCL Business Plc from 2012 to 2013, followed by a position as a Research Assistant at University College London from 2013 to 2014. These roles provided her with valuable insights and skills that she later applied in her data science career at HSBC.
Achievements
Andria Sarri has made contributions to the field of fraud detection within financial institutions, particularly through her work at HSBC. She has been involved in developing machine learning models that enhance the bank's capabilities in identifying and preventing fraudulent activities. Her efforts in the HSBC Financial Intelligence Unit focus on improving data-driven decision-making processes, which are essential for effective risk management.