Machine learning in the Context of Central Banks and Policy Analysis
In recent years, central banks have made progress in integrating (big) data analytics into their policy analysis and decision making. As part of its three-year (2017-2019) strategic plan, the BOT has increased its usage of granular data as well as enhanced its analytics capability. The BOT has also set up a new Data Analytics Unit, which works closely with various departments to extract insights from data, deepening the understanding of the economy and financial system. These initiatives aim at promoting evidence-based policy making and improving work efficiency at the BOT. In addition to surveying machine learning in the context of central banks, this session also presents several analytics use-cases at the BOT.
Mr. Liengpunsakul is Deputy Director of Data Analytics Unit at the Bank of Thailand. Set up in 2017 to enhance the Bank’s analytics capability, the Unit works closely with various departments to extract insights from data, promoting evidence-based policy making and improving work efficiency at the Bank. Prior to his current position, he has worked in various areas of the Bank, including Enterprise Risk Management Department and Statistics and Information System Department.
Mr. Liengpunsakul received an MPhil in Finance from the University of Cambridge (King’s College), and an MSc in Operations Research from Stanford University.