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

Authors Dennis Collaris , Mykola Pechenizkiy , Jarke J. van Wijk

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Dennis Collaris
  • Eindhoven University of Technology, The Netherlands
Mykola Pechenizkiy
  • Eindhoven University of Technology, The Netherlands
Jarke J. van Wijk
  • Eindhoven University of Technology, The Netherlands

Acknowledgements

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.

Cite AsGet BibTex

Dennis Collaris, Mykola Pechenizkiy, and Jarke J. van Wijk. RATE-Analytics: Next Generation Predictive Analytics for Data-Driven Banking and Insurance. In Commit2Data. Open Access Series in Informatics (OASIcs), Volume 124, pp. 8:1-8:11, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
https://doi.org/10.4230/OASIcs.Commit2Data.8

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.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Philosophical/theoretical foundations of artificial intelligence
  • Computing methodologies → Machine learning
  • Human-centered computing
Keywords
  • Visualization
  • Visual Analytics
  • Machine Learning
  • Interpretability
  • Explainability
  • XAI

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References

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