Data science in central banking: enhancing the access to and sharing of data

Committee, Irving Fisher

Data science in central banking: enhancing the access to and sharing of data

Irving Fisher Committee

No 64 in IFC Bulletins from Bank for International Settlements

Date: 2025 Written 2025-05
ISBN: 978-92-9259-850-1
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Chapters in this book:

A machine learning approach for the detection of firms infiltrated by organised crime in Italy: Pasquale Cariello, Marco De Simoni and Stefano Iezzi
Big data platform (FinPulse) initiative: Tsenddorj Dorjpurev
Central bank communication on economic activity: Sercan Eraslan and Eniko Gabor-Toth
Coding time series with machine learning: Ayoub Mharzi
Collaborating on SDMX APIs and open-source software: Brian Buffett, Stratos Nikoloutsos and Xavier Sosnovsky
Constructing high-frequency and thematic economic sentiment indicators from online news articles: applications in the Philippine context: Alan Chester Arcin, Carmelita Esclanda-Lo, Chelsea Anne Ong and Rossvern Reyes
Data Building a database on cryptocurrencies: Anamaria Illes and Ilaria Mattei
Data science in central banking: enhancing the access to and sharing of data: Bruno Tissot
Data Science in Central Banking: Enhancing the access to and sharing of data: Alessandra Perrazzelli
Data science in the context of international banking statistics: Jacob Ewertzh and John Svanäng
Data sharing using a global data registry: on a place to discover global structured time series, macro and micro data: Nelson Matt and Glenn Philip Tice
Digital transformation of financial regulators and the emergence of supervisory technologies (SupTech): a case study of the UK Financial Conduct Authority: Pavle Avramović
Do anecdotes matter? Exploring the beige book through textual analysis from 1970 to 2023: Shengwu Du, Karen Guo, Flora Haberkorn, Abby Kessler, Isabel Kitschelt, Seung Jung Lee, Anderson Monken, Dylan Saez, Kelsey Shipman and Sandeep Thakur
EMIR data for financial stability analysis and research: Michele Leonardo Bianchi, Bianca Sorvillo, Dario Ruzzi, Federico Apicella, Luigi Abate and Leonardo Del Vecchio
Error spotting with gradient boosting: a machine learning-based application for central bank data quality: Csaba Burger and Mihály Berndt
European single access point as a blueprint for global financial and green data hubs: Pawel Martyniuk and Michal Piechocki
Experiences, essentials and perspectives for data science in the hearts of central banks and supervisors: a case study of the Dutch central bank: Patty Duijm and Iman van Lelyveld
Exploring aggregation strategies for federated learning in national statistics: Mauro Bruno, Erika Cerasti, Massimo De Cubellis, Francesco Pugliese, Rafik Chemli, Benjamin Santos, Julian Templeton and Matjaz Jug
From the ML model to practice: case study on NLP-based decision-making on the eligibility of security prospectuses: Janek Blankenburg, Maximilian König, Philipp Rothhaar and Bernd Rusitschka
Future of time series: preliminary results from a BIS-IFC survey of central banks and statistical agencies: Ryland Thomas
Individual data access and sharing protection policy: definition and case study of Bank Indonesia: Johanes Iman Anugrah, Akhmad Zacky Nugraha and Sapto Widyatmiko
Invoices rather than surveys: using ML to build nominal and real indices: Pablo Acevedo, Dagoberto Quevedo, Marco Rojas, Emiliano Luttini and Matías Pizarro
Let's talk about sentiment: natural language processing using machine learning on bank earnings transcripts: Seung Jung Lee, Sriram Nagaraj, Dylan Saez, Victors Stebunovs, Cindy Vojtech and Karl Wirth
Leveraging large language models to extract data citations: Sebastian Seltmann, Emily Kormanyos and Hendrik Christian Doll
Leveraging open-source software and data standards as the backbone of your open data strategy: Darran Hodder and Matthew Nelson
New strategy of data sharing and data access in statistics: the view from Banco de Portugal: Ana R Gonçalves, Mário Lourenço, Daniel V Sousa and Thomas Verheij
Open-sourced central bank macroeconomic models: Douglas Kiarelly Godoy de Araujo
Overcoming data-sharing challenges in central banking: federated learning of diffusion models for synthetic mixed-type tabular data generation: Timur Sattarov and Marco Schreyer
Project Aurora: the power of data, technology and collaboration to combat money laundring across institutions and borders: Beju Shah
Research for all: exploring machine learning applications in generating synthetic datasets: Carmelita Esclanda-Lo, Gabriel Masangkay, Chelsea Anne Ong and Rossvern Reyes
Siamese neural networks for detecting banknote printing defects: Katia Boria, Andrea Luciani, Sabina Marchetti and Marco Viticoli
Unveiling the interconnectedness of banks in payment system: methodology, utilization, and data governance considerations: Renardi Ardiya Bimantoro, Irfan Sampe and Mohammad Khoyrul Hidayat
Why SDMX matters? A community journey towards SDMX as an AI-enabler and a data mesh enabler: Eric Anvar

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