The 2025 Corruption Perceptions Index (CPI) published by Transparency International places Pakistan 136th out of 182 countries, with an overall score of just 28 out of 100, significantly below the global average of 42, reflecting widespread perceptions of corruption across public institutions and services.
These rankings have real economic consequences. A diagnostic assessment by the IMF and the World Bank suggests that if Pakistan effectively addresses corruption and strengthens governance, its economy could see meaningful gains, potentially enhancing GDP by up to 6.5 per cent over the next five years.
The persistence of corruption in Pakistan is rooted in structural vulnerabilities: many public services rely on manual procedures and discretionary decisions, creating opportunities for bribery and favouritism; government departments operate with fragmented data systems, limiting their ability to detect irregularities; and oversight bodies often engage in reactive rather than proactive enforcement.
In this context, Artificial Intelligence (AI) and Machine Learning (ML) offer transformative potential -- not as unilateral solutions, but as powerful tools that can enhance transparency, automate risk detection and reduce opportunities for corrupt practices.
One of the most promising applications of AI in anti-corruption efforts lies in automated anomaly detection. Traditional audits, which rely on manual review and random sampling, often miss nuanced patterns of fraud in complex datasets. Machine learning models, however, can process large volumes of data and identify deviations from expected patterns.
For example, unsupervised ML techniques such as clustering algorithms and autoencoders can isolate unexpected behaviour without requiring prior examples of fraud, while supervised classifiers like Random Forests and Gradient Boosting Machines can be trained on historical cases of irregularities to predict future anomalies with high precision. These methods reduce reliance on human auditors, whose judgments may be influenced by bias or external pressure.
Pakistan’s tax authority, the Federal Board of Revenue (FBR), has begun exploring data-driven mechanisms to identify tax evasion and unregistered taxpayers, marking an initial step towards AI-enabled predictive compliance monitoring. While comprehensive evaluations of these initiatives are still emerging, the adoption of analytics frameworks indicates a shift toward more proactive detection of financial non-compliance.
Public services such as welfare programmes like Benazir Income Support Program (BISP), subsidies and official registries are also susceptible to identity fraud and record duplication, which enable corruption and the leakage of public funds. AI-based biometric verification systems built on convolutional neural networks (CNNs) and facial recognition models can substantially reduce such vulnerabilities by automatically confirming an individual’s identity against a central database.
Pakistan’s National Database and Registration Authority (NADRA) maintains an extensive repository of biometric and demographic data and deploying AI systems capable of real-time verification could minimise impersonation and duplicate registrations in government programmes, thereby reducing fraud and resource abuse.
AI-enabled e-government platforms that integrate NLP-based chatbots can also automate routine citizen interactions with public services, reducing the need for face-to-face contact with officials, a major point at which petty corruption often occurs. By ensuring consistent service delivery with non-human interactions, these systems not only improve efficiency but also create transparent procedural trails that are more resistant to manipulation.
Public contracting and procurement, historically sites of significant corruption both globally and in Pakistan, can similarly benefit from machine learning. By analysing bidding patterns across thousands of tenders, ML models can identify potentially collusive behaviour or bid manipulation. Historical price modelling using regression and time-series analysis can establish benchmark price expectations and flag bids that significantly deviate without clear justification, while network analytics can detect unusual associations among bidders -- particularly effective in environments where a limited pool of suppliers repeatedly secure contracts, potentially due to cronyism.
Despite the promise of these technologies, their effective implementation in the Pakistani context faces significant challenges. AI relies heavily on the quality and completeness of the data it uses and many public-sector datasets in Pakistan remain incomplete, inconsistent or stored in isolated silos, hindering the reliable training of models.
Government agencies also often lack the technical expertise necessary to develop, deploy and maintain advanced AI systems, and building such in-house capabilities or acquiring skilled personnel represents a major constraint.
Equally important are the legal and ethical frameworks governing the use of AI. Pakistan currently lacks comprehensive legislation on data protection and algorithmic accountability, raising concerns about misuse of AI, privacy violations and unaccountable decision-making that could paradoxically reinforce the very problems these technologies seek to address.
Technology alone cannot resolve corruption without strong institutions and sustained political will committed to transparency, accountability and ethical governance.
To realise the potential of AI and ML in combating corruption, Pakistan must adopt a multifaceted approach. Developing a national AI governance charter that mandates transparency, accountability and privacy protection in public-sector applications is essential. Investing in data infrastructure to eliminate silos and ensure integrated, high-quality datasets is a prerequisite for effective machine learning. Targeted capacity building within public institutions through training programmes, partnerships with academic institutions and public-private collaborations can strengthen technical proficiency. Implementing transparent algorithms subject to external audits by civil society organisations and independent researchers can further bolster public trust.
Corruption in Pakistan is entrenched and multifaceted, but AI and Machine Learning can serve as powerful instruments to enhance oversight, detect irregularities early and minimise opportunities for corrupt practices.
AI is not a standalone solution but a complement to robust governance structures. Its success depends on the country’s ability to build a strong data infrastructure, enact appropriate legal safeguards and foster human capacity, alongside sustained political commitment to act on insights generated by these advanced technologies. In harnessing the power of AI, Pakistan can move towards a more accountable, efficient and corruption-resilient public administration.
The writer is an AI and Machine Learning expert based in London.
