Governments increasingly seek to strengthen transparency and accountability in public financial management, yet traditional, retrospective audits struggle to surface irregularities at the speed and scale of modern procurement. This study develops and applies a practical analytics framework to U.S. Department of Commerce (DOC) procurement transactions for FY2025, demonstrating how unsupervised learning can triage large award corpora into tractable, audit-salient subsets. Using 16,581 transactions from the USAspending Award Data Archive, we engineer features aligned to established risk theories: approval lag (solicitation-to-action timing), vendor history (prior awards and concentration), award magnitude (obligations, base-and-options, potential ceilings), and award structure/competition (IDV relationships, pricing type, and extent competed)—and apply Isolation Forest (contamination = 1%). The model flags 166 atypical transactions (≈1%) characterized by (i) extreme potential award ceilings (median ≈ $8B), (ii) order-dependent pricing under Indefinite Delivery Vehicles (IDVs), and (iii) competition pathways reported as “full and open after exclusion of sources.” Sensitivity analysis shows anomalies are highly threshold-dependent (0–2% contamination yields 0–≈330 flags), underscoring the need to calibrate cutoffs to investigative capacity. While findings are not determinations of non-compliance, they delineate priority cases for follow-up testing (e.g., ceiling-to-obligation reconciliation, order-level pricing documentation, justification memos for exclusions). The framework translates directly to oversight practice via score-band triage, dashboarding, and model governance (documentation, fairness checks, periodic recalibration). Limitations include the absence of ground-truth labels and potential measurement error in administrative data; future work should integrate supervised models (e.g., logistic/ensemble learners) using adjudicated outcomes and employ explainability techniques to attribute anomaly drivers. Overall, results illustrate that predictive analytics can complement audits, reduce detection lag, and inform evidence-based policy within public procurement systems.