Vendor Risk Analytics
Description
Research on vendor risk analytic methods using supervised learning to assess contractor responsibility and whether a prospective vendor would perform successfully. AI can predict potential vendor inability to perform successfully. This capability has the potential to prevent costly, mission-impacting contracting issues. Seminal research and model training in this area was conducted by IRS and Navy personnel who published this paper (https://calhoun.nps.edu/handle/10945/62901). Subsequent, independent federally funded and academic research studies have also found that AI has the potential to improve contractor source selection decision making.
Detailed example
Vendor risk assessments output in spreadsheet or dashboard data visualization formats.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
b) Pilot – The use case has been deployed in a limited test or pilot capacity.
Expected benefit
AI assesses the likelihood of successful contractor performance and identifies vendors at heightened risk of poor performance or non-compliance.
Audit / financial statement impact
AI makes recommendations only and any contracting decisions would go through multiple layers of review by agency officials.
Controls / human review
ATO: No; PIA: Not published
Data needed
Contractor entity and contract transaction data from SAM.gov and USASpending.gov was used to train the models. Contractor entity data is provided by vendors during the contractor registration process. Contract transaction data is reported by federal agency Contracting Officers.