arbiter
JournalsPricingAccount

What drives central bank digital currency implementation? A machine-learning analysis using support vector machines and SHAP explainability

Zhanat Khishauyeva et al.

Banks and Bank Systems2026https://doi.org/10.21511/bbs.21(1).2026.13article
AJG 1
Weight
0.50

What the paper says

Type of the article: Research ArticleAbstractCentral bank digital currency (CBDC) programs have rapidly shifted from experimentation to policy-critical infrastructure decisions, yet countries show strikingly uneven progress from research to pilots and implementation. This study aims to identify and explain the key structural, macroeconomic, technological, and ecosystem-related factors that differentiate CBDC initiatives advancing to pilot or implementation stages from those remaining in early research or being discontinued across countries worldwide. Using 161 CBDC projects across 109 countries (as of December 2024) and 10 project-, public interest-, technology-, and macroeconomic indicators, we estimate a Support Vector Machines classifier with GridSearchCV (5-fold) tuning and interpret the results using Shapley Additive exPlanations explainability. The raw outcome distribution was strongly imbalanced (83.85% in the early/cancelled class), so ADASYN balancing was applied, producing 270 observations with equal class shares and an 85/15 train–test split (229/41). The optimized SVM (RBF; C = 10, gamma = 10) achieved 93.90% cross-validated accuracy and 0.88 accuracy on the test set, indicating strong predictive performance on unseen data. Test-set metrics show an informative error profile: for class 1 (advanced projects), recall = 1.00 and F1 = 0.89, while for class 0 (early/cancelled), precision = 1.00 with recall = 0.75 (macro/weighted F1 = 0.88), implying that the model identifies all advanced projects but may misclassify around one-quarter of early/cancelled cases. SHAP ranks the strongest drivers as use-case direction, inflation, crypto adoption ranking, CBDC-related research output, and international participation, with mixed/wholesale projects, higher inflation, stronger scientific attention, and greater international involvement generally increasing the likelihood of advancement.

Open paper page →

Cite this paper

https://doi.org/https://doi.org/10.21511/bbs.21(1).2026.13

Or copy a formatted citation

@article{zhanat2026,
  title        = {{What drives central bank digital currency implementation? A machine-learning analysis using support vector machines and SHAP explainability}},
  author       = {Zhanat Khishauyeva et al.},
  journal      = {Banks and Bank Systems},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.21511/bbs.21(1).2026.13},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

What drives central bank digital currency implementation? A machine-learning analysis using support vector machines and SHAP explainability

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.