event description

COMESA central banks can benefit from the use of data science to extract value from various data sets to improve their modelling and forecasting techniques for assessing vulnerabilities to financial stability and their evolution over time. Moreover, data science techniques can support other tasks that are relevant from a financial stability perspective, including payment systems analysis, financial inclusion monitoring, consumer risk analysis, and anti-money laundering monitoring.

Therefore, an introductory course on the application of data science for financial stability analysis will equip participants to leverage data science to enhance the effectiveness of financial stability analysis and complement methodologies such as stress testing. This course introduces applications of data science for typical analytical challenges such as data handling, modelling, and interpretation. The focus of the training will be on defining use cases that are relevant to the region and on the interpretability of the results obtained in relation to informing macroprudential policy. Participants will also be provided with hands-on exercises in the R programming language. 

Target Audience

The target audience for the course are Central Bank Statisticians, Economists and Financial Stability Analysts and Bank Supervisors from COMESA member countries. Participants are expected to have an advanced degree in Economics, Statistics, Mathematics, Financial Accounting, and/or relevant equivalent experience in Financial Stability and Bank Supervision.