From Sensors to Standardized Financial Reports: A Proposed Automated Accounting System Integrating IoT, Blockchain, and XBRL
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Modern advances in technology have increased the demand for traditional accounting systems to be upgraded for real-time data processing, security, and standardized reports. Thus, this paper proposes a new accounting information system that integrates IoT, blockchain, and XBRL. The proposed system aims to automate the accounting process by using IoT to collect data and send it automatically to a blockchain, which acts as a database that will generate journal entries automatically through smart contracts. XBRL will then be used as an output method for standardized financial reports based on the data transferred from the blockchain. This paper uses a qualitative research design based on semi-structured interviews with 13 industry experts from IT engineering, academia, and financial systems analysis. NVivo software was used to conduct a thematic analysis of interview transcripts. The findings demonstrated that integrating IoT, blockchain, and XBRL is technically feasible, with significant potential to enhance accounting systems. Additionally, the findings identified key challenges of the proposed system, including the complexity of integration, data validation across technologies, costs, user adoption, and scalability concerns. However, the results showed that this system offers substantial benefits, such as real-time data capture from IoT devices, secure data storage and immutability through blockchain, standardized financial reporting via XBRL, accounting process automation, improved data accuracy, and enhanced security and transparency in financial reporting. The study also identified an optimal mechanism for ensuring seamless data transmission between these technologies. The study makes a valuable contribution to the accounting field by providing a new framework for automating data collection, enhancing data security, and streamlining financial reporting, with significant potential to advance accounting systems and improve transparency, accuracy, and efficiency in financial reporting. The study’s potential to impact accounting systems and financial reporting research and practice emphasizes its importance.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it