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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…
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16–23 minutesFinancial Data Automation ROI: A Data Professional’s Guide
Read More ->: Financial Data Automation ROI: A Data Professional’s GuideThe financial services sector is experiencing a data automation revolution, with 82% of CFOs increasing investments in digital technology in 2024, yet 49% of finance departments still operate with zero automation, relying on manual data entry and Excel spreadsheets (Solvexia, 2025). For data professionals, demonstrating financial data automation ROI has become critical as organizations seek measurable value from automated data pipelines, dashboards, and reporting frameworks. The business case for data automation has never been stronger, supported by compelling evidence from international organizations. The OECD’s 2025 “SME Digitalisation for Competitiveness” survey demonstrates that digitalisation can unlock SME competitiveness by helping SMEs…
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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…
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5–7 minutesGraphRAG is The Next Evolution in Economic Data Analysis
Read More ->: GraphRAG is The Next Evolution in Economic Data AnalysisHow knowledge graphs are transforming economic analysis by connecting quantitative data with institutional insights The Challenge Every Economic Analyst Knows Too Well Economic analysis today presents a fundamental challenge: the data we need exists in two separate worlds that rarely speak to each other. On one side, we have rich quantitative datasets like the World Bank’s World Development Indicators, providing precise measurements of GDP growth, inflation rates, and unemployment figures across countries and time periods. On the other side, we have institutional reports from the IMF, OECD analyses, central bank communications, and policy papers that explain the “why” behind the…
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