A Pipeline for Integrating and Analyzing Public Business Data in Albania Using Web Scraping and Machine Learning

Abstract

The increasing availability of fragmented public business and procurement data creates opportunities for company-level empirical analysis, but limited evidence exists on how integrated procurement and administrative records can be used to characterize heterogeneous economic operators in Albania. This study investigates the analytical value of integrating public procurement records from the Public Procurement Agency (APP) with administrative business information from the National Business Center (QKB) through the unique NIPT identifier. The resulting company-level dataset is analyzed to examine whether procurement-derived characteristics contain systematic information for distinguishing heterogeneous company profiles and reproducing heuristic risk-oriented classifications. The empirical framework combines data normalization, entity-level integration, feature engineering, heuristic-label construction, supervised classification, Principal Component Analysis (PCA), K-Means clustering, anomaly detection, feature-importance analysis, and a financial-enrichment sensitivity experiment. In the 18-feature reduced experiment, HistGradientBoosting achieved the strongest mean repeated-cross-validation performance, with an F1-score of 0.8590, ROC AUC of 0.9683, and Average Precision (AP) of 0.9399. The analysis further identifies distinct company profiles characterized by differences in procurement intensity, contract value, and economic exposure, including small groups with exceptionally high activity and atypical characteristics. Financial enrichment did not produce a substantial improvement in classification performance, indicating that procurement-derived variables provide the dominant analytical signal in the current dataset. Across 999 deterministic label permutations, the mean ROC AUC was 0.4999, and the mean AP was 0.2700; the one-sided empirical p-value was 0.001 for both metrics. This diagnostic does not prove the absence of leakage, overfitting, or residual proxy dependence. The findings provide empirical evidence that company-level integration of procurement and administrative data can reveal heterogeneous procurement profiles and atypical patterns relevant to procurement monitoring, public-sector analytical capacity, and data-driven governance. Because the target labels are heuristic and are not independently validated against official risk outcomes, the results are interpreted as exploratory evidence for company profiling and risk-oriented analysis rather than as formal assessments of business risk, misconduct, or performance.