MétaCan
Menu
Back to cohort

Real-Time Internal Control Monitoring in Public Sector Entities: A Framework Grounded in International Standards on Auditing

2023· article· W7164034217 on OpenAlexaboutno aff
Nyiawung Fobellah Abetoh

Bibliographic record

VenueInternational Journal of Multidisciplinary Research and Growth Evaluation · 2023
Typearticle
Language
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsnot available
Fundersnot available
KeywordsInternal auditInternal controlPublic sectorAuditInformation technology auditControl environmentAudit planChief audit executiveJoint audit

Abstract

fetched live from OpenAlex

The adequacy of internal control systems in public sector entities has been a central concern of public financial management reform across developed and developing economies for more than four decades. Despite sustained investment in internal control frameworks, legislative audit mandates, and public financial management reform programs, control failures resulting in financial misappropriation, waste, and misreporting continue to represent a pervasive challenge for governments at all levels. Traditional approaches to internal control assessment, anchored in periodic evaluations conducted by internal audit units and annual financial audit procedures, are inherently retrospective and limited in their ability to detect control failures in real time before they result in material financial loss. This paper proposes a comprehensive framework for real-time internal control monitoring (RTICM) in public sector entities, grounded in the conceptual and procedural requirements of the International Standards on Auditing (ISAs) issued by the International Auditing and Assurance Standards Board (IAASB) and the International Standards of Supreme Audit Institutions (ISSAIs) promulgated by the International Organization of Supreme Audit Institutions (INTOSAI). The framework integrates continuous transaction monitoring, automated exception reporting, rule-based control testing, and machine learning-driven anomaly detection into a structured real-time oversight architecture designed for deployment across national government ministries, departments, and agencies. The framework is developed through a systematic integration of three methodological streams: analysis of internal control failures documented in public sector audit reports from twenty countries over a twelve-year period (2010-2022); synthesis of relevant provisions from ISA 315, ISA 330, ISA 402, ISSAI 1315, ISSAI 1330, and the COSO Internal Control Integrated Framework; and case study examination of real-time monitoring deployments in the public sectors of Estonia, South Korea, Canada, New Zealand, and South Africa. The resulting framework, designated the Public Sector Real-Time Monitoring Architecture (PS-RTMA), comprises five integrated subsystems: a transaction ingestion and normalization engine, a real-time rule execution layer, an anomaly scoring and prioritization module, an exception workflow management system, and a management reporting and dashboard interface. Conceptual evaluation of the PS-RTMA framework against documented control failure patterns in the public sector audit literature suggests that the integrated five-subsystem architecture would be expected to detect a substantially higher proportion of material control failures within twenty-four hours of their occurrence, compared to the extended detection lags characteristic of traditional annual audit cycles. The projected detection timeliness improvement reflects the fundamental architectural advantage of continuous population-level monitoring over periodic sample-based examination: by observing all transactions in real time rather than examining a subset retrospectively, the PS-RTMA eliminates the detection gap created by the interval between audit cycles. The magnitude of this improvement in practice will depend on the quality of real-time data feeds, the calibration of the anomaly detection subsystem, and the operational capacity to investigate generated alerts within the target detection window.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.032
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.032
Threshold uncertainty score0.170

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0320.034
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0090.007
Science and technology studies0.0020.014
Scholarly communication0.0120.012
Open science0.0030.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.066
GPT teacher head0.386
Teacher spread0.321 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Multidisciplinary Research and Growth EvaluationSame topicAuditing, Earnings Management, GovernanceFrench-language works237,207