RATE-Analytics: Next Generation Predictive Analytics for Data-Driven Banking and Insurance

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Abstract

We conducted the RATE-Analytics project: a unique collaboration between Rabobank, Achmea, Tilburg and Eindhoven University. We aimed to develop foundations and techniques for next generation big data analytics. The main challenge of existing approaches is the lack of reliability and trustworthiness: if experts do not trust a model or its predictions they are much less likely to use and rely on that model. Hence, we focused on solutions to bring the human-in-the-loop, enabling the diagnostics and refinement of models, and support in decision making and justification. This chapter zooms in on the part of the project focused on developing explainable and trustworthy machine learning techniques.
Original languageEnglish
Title of host publicationCommit2Data
EditorsBoudewijn R. Haverkort, Aldert de Jongste, Pieter van Kuilenburg, Ruben D. Vromans
PublisherSchloss Dagstuhl - Leibniz-Zentrum für Informatik
Chapter8
Pages8:1-8:11
Number of pages11
ISBN (Electronic)978-3-95977-351-5
DOIs
Publication statusPublished - 28 Oct 2024

Publication series

NameOpenAccess Series in Informatics (OASIcs)
Volume124
ISSN (Electronic)2190-6807

Funding

We would like to thank numerous colleagues of the RATE project team at Tilburg University, Rabobank, Achmea, and TU/e who over the years provided endless support and facilitated collaboration. We would like to thank NWO, Rabobank and Achmea for the provided funding. This work is part of the research programme Commit2Data, specifically the RATE Analytics project with project number 628.003.001. Last, but not least we would like to thank the reviewers for providing constructive feedback.

FundersFunder number
Nederlandse Organisatie voor Wetenschappelijk Onderzoek628.003.001
Tilburg University

    Keywords

    • Visualization
    • Visual Analytics
    • Machine Learning
    • Interpretability
    • Explainability
    • XAI
    • Explain-ability

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