@article{10.11648/j.sdcomput.20260101.12,
  author = {Tigist Mintesnot Tewabe},
  title = {AI-Driven Insights for Detecting Corruption Risks in Ethiopian Public Sectors: A Systematic Literature Review},
  journal = {Science Discovery Computers},
  volume = {1},
  number = {1},
  pages = {15-19},
  doi = {10.11648/j.sdcomput.20260101.12},
  url = {https://doi.org/10.11648/j.sdcomput.20260101.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sdcomput.20260101.12},
  abstract = {This systematic literature review examines AI-driven methods and evidence for detecting corruption risks in the public sectors of Ethiopia and neighboring East African countries. Corruption remains a significant challenge to effective public-sector governance, economic development, and the efficient delivery of public services in Ethiopia and East African countries. The increasing availability of digital government data and advances in artificial intelligence (AI) provide new opportunities to identify corruption challenges, detect anomalies, and strengthen transparency and accountability. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-guided search, screening, and selection procedures, relevant studies were identified from Scopus, Web of Science, PubMed, Google Scholar, and regional grey literature repositories. The review synthesizes evidence from 28 studies published between 2010 and 2025 that investigate the application of AI, machine learning (ML), network analysis, anomaly detection, and related computational approaches to corruption-risk identification. The review targeted on four major public-sector domains: public procurement, financial auditing, payroll management, and asset management. Key themes examined include the types and quality of data used, pre-processing and feature-engineering practices, dominant AI and ML algorithms, evaluation metrics, model interoperability, and the practical feasibility of implementation in low-resource environments. The results indicate growing interest in AI-enabled corruption detection, particularly through anomaly detection, classification, predictive modeling, and network-based approaches. However, the evidence base remains limited by fragmented datasets, insufficient data quality, limited access to government records, insufficient technical capacity, and the absence of standardized evaluation frameworks. Ethical concerns, including privacy, algorithmic bias, transparency, accountability, and the potential misuse of automated decision-support systems, also require careful consideration. In general, AI can complement, rather than replace, institutional anti-corruption mechanisms. For Ethiopia, priority should be given to strengthening public-sector data infrastructure, improving data governance and interoperability, developing interpret able and context-sensitive AI models, and establishing controlled pilot projects for procurement and financial-integrity monitoring. Sustained collaboration among policymakers, anti-corruption agencies, universities, technology experts, and international partners, together with capacity building and ethical safeguards, is essential for responsible and effective adoption of AI-driven corruption-risk detection systems.},
 year = {2026}
}

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TY  - JOUR
T1  - AI-Driven Insights for Detecting Corruption Risks in Ethiopian Public Sectors: A Systematic Literature Review
AU  - Tigist Mintesnot Tewabe
Y1  - 2026/09/22
PY  - 2026
N1  - https://doi.org/10.11648/j.sdcomput.20260101.12
DO  - 10.11648/j.sdcomput.20260101.12
T2  - Science Discovery Computers
JF  - Science Discovery Computers
JO  - Science Discovery Computers
SP  - 15
EP  - 19
PB  - Science Publishing Group
UR  - https://doi.org/10.11648/j.sdcomput.20260101.12
AB  - This systematic literature review examines AI-driven methods and evidence for detecting corruption risks in the public sectors of Ethiopia and neighboring East African countries. Corruption remains a significant challenge to effective public-sector governance, economic development, and the efficient delivery of public services in Ethiopia and East African countries. The increasing availability of digital government data and advances in artificial intelligence (AI) provide new opportunities to identify corruption challenges, detect anomalies, and strengthen transparency and accountability. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-guided search, screening, and selection procedures, relevant studies were identified from Scopus, Web of Science, PubMed, Google Scholar, and regional grey literature repositories. The review synthesizes evidence from 28 studies published between 2010 and 2025 that investigate the application of AI, machine learning (ML), network analysis, anomaly detection, and related computational approaches to corruption-risk identification. The review targeted on four major public-sector domains: public procurement, financial auditing, payroll management, and asset management. Key themes examined include the types and quality of data used, pre-processing and feature-engineering practices, dominant AI and ML algorithms, evaluation metrics, model interoperability, and the practical feasibility of implementation in low-resource environments. The results indicate growing interest in AI-enabled corruption detection, particularly through anomaly detection, classification, predictive modeling, and network-based approaches. However, the evidence base remains limited by fragmented datasets, insufficient data quality, limited access to government records, insufficient technical capacity, and the absence of standardized evaluation frameworks. Ethical concerns, including privacy, algorithmic bias, transparency, accountability, and the potential misuse of automated decision-support systems, also require careful consideration. In general, AI can complement, rather than replace, institutional anti-corruption mechanisms. For Ethiopia, priority should be given to strengthening public-sector data infrastructure, improving data governance and interoperability, developing interpret able and context-sensitive AI models, and establishing controlled pilot projects for procurement and financial-integrity monitoring. Sustained collaboration among policymakers, anti-corruption agencies, universities, technology experts, and international partners, together with capacity building and ethical safeguards, is essential for responsible and effective adoption of AI-driven corruption-risk detection systems.
VL  - 1
IS  - 1
ER  - 

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