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.
As part of Data Analytics Unit, Mr. Chinnawat Devahastin Na Ayudhya is a data scientist at the Bank of Thailand. He graduated with First Class Honors from Chulalongkorn University in Computer Engineering. Pursuing a specialism course in Artificial Intelligence, Mr. Devahastin Na Ayudhya received an MSc degree with Distinction from Imperial College London in 2015. He works closely with business people to deliver intriguing insights and economic indicators from micro data using Python, R, Hadoop, Tableau, Gephi, and whatever it takes to convey meaningful results to users.