What is it about?

Government defence contracts frequently involve complex relationships between major contractors and subcontractors. Can financial information help identify patterns in these relationships? This study examines 10 years of financial and contracting data from 32 US defence companies. Using interpretable machine learning models, we investigate which financial characteristics are most useful for predicting subcontracting intensity. The results highlight the importance of cost structures, operating margins, depreciation, and other accounting indicators. The study demonstrates how financial information can support transparent and systematic assessments of subcontracting behavior.

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Why is it important?

Public defence procurement involves substantial financial resources and requires effective oversight. The research provides a practical foundation for risk-based monitoring, allowing procurement authorities, auditors, and financial controllers to identify contracts or companies that may warrant closer examination. Interpretable predictive models can strengthen transparency and support more informed public accountability without treating statistical risk signals as proof of misconduct.

Perspectives

Public accountability increasingly depends on the ability to analyze complex financial information. Our study shows how interpretable machine learning can help transform accounting data into useful signals for procurement oversight. Predictive analytics should support human judgment and transparent public-sector governance rather than replace established auditing procedures.

Phd. Ricardo Teruel-Gutiérrez
Universidad Politecnica de Cartagena

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This page is a summary of: Financial governance and subcontracting in US defence firms: a predictive model for public accountability, Public Money & Management, October 2025, Taylor & Francis,
DOI: 10.1080/09540962.2025.2579076.
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