Introduction
The U.S. Department of Housing and Urban Development (HUD) plays a critical role in providing affordable housing and urban development programs across the country. In addition, HUD must respond to housing crises in a timely fashion, while ensuring applicants are compliant and eligible with program regulations. Today’s systems can make it difficult to balance the speed of the screening process with the need to deliver the benefits.
Artificial intelligence (AI) has emerged as a powerful tool in addressing these challenges, with potential use cases that can enhance HUD’s efficiency and effectiveness. In this article, we’ll explore key AI use cases within HUD and how TrackLight’s innovative solutions can provide targeted assistance.
HUD Challenges
HUD oversees programs such as Section 8 housing and Federal Housing Administration (FHA) loans, which are particularly vulnerable to fraud. According to a recent audit by the HUD Office of Inspector General (OIG), HUD has failed for the seventh consecutive year to estimate improper payments in two of its largest rental assistance programs—the Tenant-Based Rental Assistance (PIH-TBRA) and Project-Based Rental Assistance (PBRA) programs. Together, these programs account for over 67% of HUD’s $45.3 billion expenditures in FY23, putting hundreds of billions of taxpayer dollars at heightened risk of fraud, waste, and abuse. The last compliant estimate of improper payments was in 2016, at $1.7 billion, highlighting HUD’s ongoing struggle to resolve systemic issues. Inspector General Rae Oliver Davis emphasized the need for coordinated efforts to address these challenges to safeguard taxpayer funds and ensure the integrity of federal programs.
Source: GAO, 2024 and HUD, 2024.
AI Use Cases in HUD
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Fraud Detection: AI can sift through massive datasets, identifying suspicious patterns and anomalies in applications, financial records, and transactions. This makes fraud detection faster and more reliable than manual processes.
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Risk Scoring: AI-powered risk scoring models can assign risk levels to applicants and contractors, helping HUD allocate resources more effectively.
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Data Validation and Integration: AI can assist HUD in validating data across multiple programs and systems, ensuring consistency and reducing errors.
TrackLight’s Role
TrackLight’s AI tools bring vast data and advanced analytics directly to HUD’s decision-makers. Our solutions analyze multiple data sources to provide a comprehensive risk score for applicants and contractors. HUD can use TrackLight’s platform to:
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Prevent fraud before it happens, saving tax dollars by ensuring only qualified applicants and vendors receive and provide assistance.
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Improve decision-making and operational efficiency by surfacing key insights.
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Identify, triage, and prioritize potential fraud that has occurred.
Case Study Example
Imagine a HUD inspector tasked with verifying an applicant’s eligibility for a housing assistance program, such as Section 8. Traditionally, this process would require the inspector to navigate a complex network of databases—cross-referencing income statements, rental history, criminal background checks, and other documentation. Each data source may reside in a different system, making it difficult to efficiently gather and validate the necessary information. This manual process, which often involves piecing together fragmented data, can be time-consuming and prone to errors. Even small mistakes in eligibility verification can result in improper payments or, worse, fraud slipping through the cracks.
Enter TrackLight:
With TrackLight, the inspector’s workflow changes dramatically. TrackLight’s AI platform integrates all relevant data into a single, streamlined dashboard. Using machine learning algorithms trained on thousands of fraud patterns, the system automatically scans the applicant’s information in real time, checking for discrepancies such as unreported income, mismatches in rental history, or potential conflicts of interest. The system provides an instant, comprehensive risk score for the applicant, factoring in known fraud schemes and anomalous behavior.
For example, if the applicant has a rental history that doesn't match their reported income, or if there’s a pattern of submitting incomplete or inconsistent documentation, TrackLight’s platform will flag these issues and assign a higher risk score. This allows the inspector to focus on high-priority cases rather than manually reviewing every single file, saving hours of work. Instead of being bogged down by manual tasks, the inspector can immediately investigate the flagged issues before any funds are disbursed, reducing the risk of improper payments or fraud.
Outcome: The inspector, equipped with this real-time AI-driven insight, not only improves the efficiency of the screening process but also enhances the integrity of HUD’s housing assistance programs. By focusing their attention on high-risk cases flagged by TrackLight, inspectors can prevent fraud before it occurs, ensuring that only eligible and qualified applicants receive assistance. In addition, this process provides HUD leadership with detailed analytics on emerging fraud patterns, enabling them to fine-tune program regulations and improve overall governance.
TrackLight’s proactive fraud detection not only prevents potential fraud from happening but also helps HUD meet compliance standards more effectively. By automating many of the labor-intensive processes, HUD can save significant resources and reinvest them into improving program outcomes, providing housing assistance to more people in need.
Conclusion
By supporting inspectors with AI, HUD can reduce fraud, improve program integrity, and ensure efficient use of resources. TrackLight’s AI-powered solutions provide HUD with the tools it needs to protect taxpayer dollars while delivering housing assistance to those who need it most.
TrackLight is committed to helping government agencies like HUD stay ahead of fraud. For more information, learn about our Due Diligence solution.
