Data & Replication
All data, scripts, and outputs are available in the project repository for full replication.
Repository
GitHub: Digital-AI-Finance/Systemic-Risk-Channels-in-Digital-Finance
Clone and run the scripts to reproduce all tables, rankings, and sensitivity analyses reported in the paper.
Key Data Files
| File | Description |
|---|---|
output/data/channel_rankings.json |
Composite scores, sub-scores, and ranks for all 14 candidate channels (unscreened corpus) |
output/data/channel_rankings_filtered.json |
Rankings recomputed after removing off-topic papers identified by relevance screening |
output/data/sensitivity_analysis.json |
Four alternative weight schemes with per-channel composite scores and ranks |
output/data/filtered_vs_unfiltered_comparison.json |
Side-by-side comparison of filtered and unfiltered rankings with rank changes |
output/data/corpus_contamination_audit.json |
Audit of off-topic papers per channel: counts removed, reasons, and papers remaining |
output/data/openalex_merged.json |
Merged OpenAlex corpus with all 2,433 papers, metadata, and channel assignments |
output/data/per_paper_scoring_detail.json |
Per-paper scoring breakdown: relevance flags, citation counts, channel membership |
output/data/key_papers.json |
Curated list of high-impact papers per channel used for narrative synthesis |
Key Scripts
| Script | Purpose |
|---|---|
scripts/channel_mapper.py |
Computes composite scores and rankings for all 14 channels from the unscreened corpus |
scripts/channel_mapper_filtered.py |
Recomputes rankings after removing off-topic papers; produces filtered comparison |
scripts/openalex_search.py |
Queries OpenAlex API with Boolean keyword expressions; retrieves and deduplicates papers |
scripts/sensitivity_analysis.py |
Tests four weight schemes and reports rank stability across configurations |
scripts/verify_all_papers.py |
Validates every paper in the corpus against OpenAlex metadata (DOI, title, year) |
scripts/hostile_review.py |
Automated adversarial review: checks for citation errors, data inconsistencies, and methodology gaps |
scripts/reference_audit.py |
Cross-references every in-text citation against the bibliography; flags orphans and phantoms |
Paper Downloads
Full Paper
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Journal Version
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Citation
Osterrieder, Jörg (2026). Systemic Risk Channels in Digital Finance: A Comprehensive Taxonomy. Working Paper. University of Twente.