Blockchain analytics and artificial intelligence
Abstract
Blockchain records track information about financial payments, movements of products through supply chains, identity verification information, and many other assets. Analytics on this data can provide provenance histories, predictive planning, fraud identification, and regulatory compliance. In this paper, we describe analytics engines connected to blockchains to provide easy-to-use configurable dashboards, predictive models, provenance histories, and compliance checking. We also describe how blockchain data can be combined with external data sources for secure and private analytics, enable artificial intelligence (AI) model creation over geographically dispersed data, and create a history of model creation enabling provenance and lineage tracking for trusted AI.
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Research has begun exploring DLT for auditing purposes in organizational contexts, and first results indicate feasibility of DLT for auditing AI.
“Owing to DLTs characteristics (e.g., decentralization, high tamper resistance), research has recently begun exploring the application of DLT for auditing purposes in organizational contexts (Hofman et al. 2019), while first research results also indicate the feasibility of DLT for the auditing of AI (Dillenberger et al. 2019).”
From Trustworthy artificial intelligence (2021) · cites this paper as Dillenberger et al. 2019
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Brodzik, A., & Mazurczyk, W. (2025) · Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit) · doi:10.1109/eucnc/6gsummit63408.2025.11036922
Jaiswal, A. K., & Tang, T. (2026) · Springer eBooks · doi:10.1007/978-3-032-06046-4_9
Sanghvi, M., Ubarhande, P., Chandani, A., Pathak, M., Wagholikar, S., Bagade, S. J., & Atiq, R. (2026) · Frontiers in Blockchain · doi:10.3389/fbloc.2026.1730387 · Cited by 1 · see citing works
Risk-Based Auditing (RBA) in Global Supply Chains
Jayaraman, A. V., Dolly Jain, K., & Thulasi, V. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-9063-5.ch011
- Authors
- Dillenberger, D. N., Novotny, P., Zhang, Q., Jayachandran, P., Gupta, H., Hans, S., Verma, D., Chakraborty, S., Thomas, J. J., Walli, M. M., Vaculin, R., & Sarpatwar, K.
- ORCID
- Venue
- IBM Journal of Research and Development
- Publisher
- IBM
- Published
- 2019-03
- Volume
- 63
- Issue
- 2/3
- Pages
- 5:1-5:14
- ISSN
- 0018-8646 (ISSN-L)
- OpenAlex
- W2915382319
- Cited by
- 82
- References
- 27
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 7 · 2023: 13 · 2022: 17 · 2021: 13 · 2020: 14 · 2019: 6 · 2018: 1
- FWCI
- 15.56
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IBM Journal of Research and Development (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology in Education and Learning
- Keywords
- blockchain analytics, artificial intelligence, blockchain data, provenance tracking, predictive modeling, fraud detection, regulatory compliance, external data integration, trustworthy AI
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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