Development Of a Machine-Learning Approach for Internal-Control Weakness Prediction and Audit-Risk Scoring In U.S. State Government Agencies
Mohammad Abir Chowdhury Shovon · 2026
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
This study has investigated a machine-learning approach for predicting internal-control weaknesses and supporting audit-risk scoring in U.S. state government agencies. The study has examined whether machine-learning capability, internal-control data quality, organizational audit readiness, and auditor analytics competency influence internal-control weakness prediction effectiveness and audit-risk scoring effectiveness. A quantitative, cross-sectional, case-study-based design has been used with 286 valid responses from public-sector professionals, yielding an 86.67% response rate from 330 distr
Abstract by Mohammad Abir Chowdhury Shovon (2026) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.63125/3v722074