Pakistan’s efforts to use artificial intelligence and advanced data analysis to identify collusion in public procurement have received international recognition. The Competition Commission of Pakistan’s (CCP) Bid-Rigging Detection System (BRDS) has been featured in Computational Antitrust Worldwide: Fifth Cross-Agency Report, published by Stanford Computational Antitrust.
The report presents first-hand accounts from 30 competition authorities worldwide, examining how computational tools can strengthen enforcement and uncover practices that conventional methods may struggle to detect.
AI-Based System to Transform Bid-Rigging Detection
According to the report, BRDS is designed to shift Pakistan’s approach to detecting collusive tendering from a largely complaint-driven, manual process towards proactive, systematic and data-led market surveillance.
The system aims to analyse large volumes of procurement data to identify suspicious bidding patterns, including bid rotation, cover bidding and participation by related parties across multiple tenders.
These practices can undermine competition, restrict fair participation and increase the cost of government contracts. Automated screening could help authorities identify potential risks earlier and focus investigations on cases requiring closer scrutiny.
Pakistan’s Public Procurement Spending Highlights Need for Automation
The initiative is particularly significant given the scale of public procurement in Pakistan. The country is estimated to spend approximately 15–20% of its gross domestic product, or around $60 billion annually, on procuring goods, works and services.
Historically, fragmented procurement records and the absence of automated links with corporate ownership information have made it difficult to detect sophisticated collusion across different tenders and procuring entities.
BRDS seeks to address these challenges by enabling the systematic analysis of procurement records and helping identify relationships and patterns that may not be apparent through the manual examination of individual bids.
CCP Advances Inter-Agency Coordination for Implementation
The system has reached an advanced stage of development and pre-deployment preparation. The CCP has reached understandings with the Public Procurement Regulatory Authority, provincial procurement authorities and the Securities and Exchange Commission of Pakistan (SECP) regarding its implementation.
Access to structured, machine-readable procurement information and relevant corporate records will be important to the system’s ability to identify potential risks across datasets.
CCP Chairman Farid Ahmad Tarar said Pakistan could not effectively monitor procurement worth trillions of rupees through complaints and manual tender reviews alone. He said BRDS would help the Commission identify complex and concealed bidding patterns across large datasets and direct investigative resources towards the highest-risk cases.
Report Highlights Lessons for Developing Economies
The CCP’s contribution also outlines practical lessons for competition authorities in developing economies. These include investing in machine-readable data, strengthening cooperation between public institutions, conducting controlled testing and ensuring that analytical findings can withstand legal scrutiny.
These considerations are important because automated detection systems can flag suspicious patterns, but enforcement decisions still require appropriate investigation, evidence assessment and due process.
The report has been made available through the Social Science Research Network (SSRN), a widely used research platform covering law, economics and the social sciences. Its publication brings Pakistan’s experience to the attention of the international regulatory and academic community, allowing other competition authorities to examine the country’s developing computational-enforcement model.
BRDS Expected to Strengthen Competition Enforcement
The CCP expects BRDS to strengthen its efforts against cartelisation by enabling proactive screening of procurement data, earlier identification of potentially high-risk bidding patterns and more evidence-based prioritisation of investigations.
If implemented effectively, the system could improve the detection of collusive practices, support fairer competition in public procurement and help public institutions secure better value for money.
Its impact will depend on data quality, effective inter-agency coordination, reliable analytical methods and the Commission’s ability to translate identified risks into legally sustainable enforcement action.