Final Check, Fraud in Check: Hindsight into Foresight
The U.S. Government Accountability Office (GAO) has estimated that, for fiscal years 2018 through 2022, the U.S. government’s total direct annual financial losses will range from $233 billion to $521 billion. [1] This staggering amount encompasses various types of fraud, including grants.
Primary Stage of Fraud Occurrence
The majority of fraudulent activities transpire after the award is granted, specifically during the active award phase (also known as post-award), when the award has been conferred, work is in progress, and financial transactions are ongoing. Granting agencies utilize a range of procedures and tools to identify fraud during this post-award period; however, these measures are predominantly reactive rather than proactive.
Lack of Structured Fraud Data Compilation
Given the wide variety of grant fraud, agencies may encounter different types of fraud across grant programs. Some of the common categories of fraud encountered are:
- Program fraud
- Procurement fraud
- Misallocation of funds
- Nepotism
Diverse, disconnected, unstructured data on fraud originating from multiple sources and in incompatible formats is lost without a standardized storage and reporting framework. The type and nature of the fraud, along with other associated details, aren’t captured in a format that can be used to perform future checks against grantees.
Closeout Evaluation: Data with a Strong Purpose
Closeout evaluation isn’t merely a report or a formality to be completed. It is a powerful tool that captures comprehensive feedback on the grant. When used as intended within the specified timelines, it serves as a one-stop shop for highlighting the grantee’s operational, financial, and behavioral performance on the grant, which ultimately helps prevent future grant fraud. By capturing potential red flags in a structured format across these categories, the evaluation clearly documents deviations from standard grant management procedures. The recommendations below are not exhaustive, but are intended to guide practical, detailed action for documenting abuse, fraud, or waste concerns in a structured, consolidated format.
- Financial Indicators:
- Spending arrangement (especially at award end)
- Reallocation of funds without conforming to procedures
- Frequent budget revisions
- Operational Indicators:
- Poor and/or missing documentation, patterns indicating false deliverables
- Deviations in reporting requirements and monitoring results
- Unverifiable/false reporting on outcomes
- In the case of “Feeding Our Future,” multiple entities were created to open Federal Child Nutrition Program sites throughout Minnesota, falsely claiming to serve thousands of children within days or weeks of formation
- Behavioral Indicators:
- Repeated late submissions, multiple resubmissions, and corrections of documents
- Irregular conduct or inconsistencies observed on a consistent basis
- Unresolved issues or concerns with overall performance
A grant applicant’s past performance can be evaluated either manually or using newer technologies, such as Artificial Intelligence (AI). A standardized format leveraged by AI can perform a quantitative risk analysis. It can generate risk scores for each grantee based on its analysis of the evaluation categories.
To conclude, well-structured, properly completed closeout evaluations not only facilitates the closure process but also serve as vital input for assessing the grantee in future grant applications. Using the grantee’s Unique Entity ID (UEI), agencies can review previous closeout evaluations and generate a summary of past performance and related metrics to evaluate the grantee’s future proposals. This creates a complete cycle in which closeout evaluations inform the risk assessment during the pre-award phase.
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Bharadwaj (B) Raghuram, PMP, CSM, is a program manager at I&I Software Inc. with more than 20 years of experience in the private and public sectors. He currently advises state and local governments, with a focus on program and technology transformations, particularly the modernization of grant initiatives. His current work emphasizes improving grant programs through AI-powered fraud detection and prevention. He may be reached at bharadwajraghuram7@gmail.com
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[1] Fraud Risk Management: 2018-2022 Data Show Federal Government Loses an Estimated $233 Billion to $521 Billion Annually to Fraud, Based on Various Risk Environments – GAO-24-105833 – Published: Apr 16, 2024. Publicly Released: Apr 16, 2024, 2.