United States Department of Housing and Urban Development, Compliance Plan for OMB Memorandum M-24-10
Compliance Plan, September 24, 2024
AI Inventory
| Pursuant to Executive Order (EO) 13960, "Promoting the Use of Trustworthy Artificial Intelligence in the Federal Government", HUD is required to inventory its Artificial Intelligence (AI) use cases and share its inventories with other government agencies and the public. In response, HUD has identified the following AI use case: | |
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Consolidated Plan Pilot Analysis. In March 2023, PD&R began a pilot project to analyze aspects of HUD's Consolidated Plans. HUD requires grantees of its formula block grant programs to submit Consolidated Plans, which are meant to identify and assess affordable housing and community development needs and market conditions. These plans are publicly available via HUD's website. HUD staff currently review these plans for compliance, but HUD lacks the capacity to do in-depth analysis of commonalities or trends contained within plans. This pilot project will explore creating a database and chat-bot that will enable HUD staff to query features of the nearly 1,000 active Consolidated Plans. This pilot exercise has the potential to inform grantees, technical assistance, and other programmatic tweaks, as well as inform how advanced data science tools can benefit our programs and operations. |
Ginnie Mae Use Cases
- Automating Draft Counterparty Credit Narrative Reports. Ginnie Mae performs counterparty credit reviews of mortgage issuers who participate in Ginnie Mae’s program. The reviews are based on written analysis and financial data. As an initial step in counterparty credit reviews, Ginnie Mae uses Natural Language Generation (NLG) to implement coded rules and generate draft narratives. This application of AI enables processing efficiency and reduces errors that can occur in a manual process.
- Counterparty Risk Anomaly Detection. Ginnie Mae is responsible for analyzing counterparty risk profiles of mortgage issuers who participate in Ginnie Mae’s program. Ginnie Mae analyzes data from multiple sources to identify potential risks and areas of focus. To enhance the identification of data patterns, Ginnie Mae uses machine learning algorithms, specifically clustering and genetic techniques. These algorithms detect potential risk areas, enabling a focused approach to subsequent analysis.
- Subledger Data Quality Machine Learning. Ginnie Mae analyzes Master Sub-Servicer (MSS) transaction data on a monthly cadence. Through its use of machine learning models, Ginnie Mae has enhanced its ability to identify data inconsistencies and exceptions associated with its MSS transaction data. By automating the analysis of a significant number of MSS transactions, the machine learning algorithms enhance the efficiency and accuracy of key reporting processes.