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Making suptech work: evidence on the key drivers of adoption

Author

Listed:
  • Leonardo Gambacorta
  • Nico Lauridsen
  • Samir Kiuhan-Vásquez
  • Jermy Prenio

Abstract

This paper examines the institutional drivers of adopting supervisory technology (suptech) by financial authorities worldwide. Using survey data from 112 financial authorities across 97 countries from the State of SupTech Report, we analyse how organisational characteristics and strategic frameworks shape the adoption of suptech initiatives. The analysis employs a two-stage hurdle model to track adoption from proof of concept to prototype, and finally to full deployment. We find that authorities with institution-wide strategies for digital transformation, data governance, and suptech deployment use, on average, about 20 additional applications and face fewer design and implementation challenges. Furthermore, while an authority's size and institutional mandate are significant factors in initiating advanced projects, the establishment of a dedicated suptech unit is the most critical factor in increasing the number of deployed applications. Finally, we find that public cloud adoption is associated with a higher probability of implementing AI tools, while reliance on in-house development is strongly associated with early-stage AI experimentation.

Suggested Citation

  • Leonardo Gambacorta & Nico Lauridsen & Samir Kiuhan-Vásquez & Jermy Prenio, 2025. "Making suptech work: evidence on the key drivers of adoption," BIS Working Papers 1309, Bank for International Settlements.
    • Handle: RePEc:bis:biswps:1309
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    JEL classification:

    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities

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