-
18–27 minutesHow to Create and Validate Synthetic Financial Data in Python
Read More ->: How to Create and Validate Synthetic Financial Data in PythonRecently Data Sense published an article discussing how synthetic financial data is reshaping risk management in financial services. We detailed how financial regulators have begun to experiment and publish guidelines for implementing and assessing synthetic data for analytical fidelity and privacy preservation. But how can this actually be achieved? Extending our previous research, we have provided a framework below for economists, supervisors and financial data scientists to implement and assess synthetic data use cases. The objective of this tutorial is to help economists, supervisors, and financial data scientists gain practical experience in generating, validating, and assessing synthetic financial data using…
-
42–63 minutesHow to use GraphRAG for Economic Data Analysis (Tutorial)
Read More ->: How to use GraphRAG for Economic Data Analysis (Tutorial)This tutorial details how to create a GraphRAG (Graph-based Retrieval Augmented Generation) to conduct economic data analysis. It will focus on combining World Bank Data with Unstructured Reports. Introduction In today’s data-driven world, economic analysts are plagued with information in various forms. This can create a significant challenge in being able to extract valuable insights that are scattered across structured databases and unstructured documents. While the World Bank’s World Development Indicators (WDI) provide rich quantitative data, the context and explanations for economic trends often lie within IMF reports, OECD analyses, and policy papers. Traditional Retrieval-Augmented Generation (RAG) systems struggle to…
CATEGORY ARCHIVES