PRAC Advanced Data Analytics

We use leading-edge analytics tools to provide investigative support and share data, services, and best practices to help Offices of Inspectors General (OIG) and federal law enforcement partners fight fraud.

map of the U S constructed of computer monitors with an animated magnifying glass scanning over the map and finding many blue and one red screen when the magnifying glass passes over them

We built our data analytics tools to support the oversight and law enforcement communities during the pandemic and for the future. As a cross-program, cross-agency solution, our data analytics proactively identify potential fraud in federal funding.

Data Analytics by the Numbers

We have more than 60 public, non-public, and commercial data sources, providing access to more than 1 billion records. As of January 2026, we have supported more than 50 OIG and federal law enforcement partners in more than 1,200 pandemic-related investigations involving over 24,000 subjects and $2.5 billion in potential fraud.

60+ data sources

1 billion+ records

50+ federal partners

1,200+ investigations

24,000+ subjects

$2.5 billion in fraud

Analytics Highlights

A stack of Social Security Cards sits beside a stack of money.

1. Fraud Prevention Alert: Pre-Award Vetting Could Have Prevented Over $79B in Potentially Fraudulent Payments

We analyzed applications across major pandemic relief programs and estimate these programs disbursed approximately $79 billion to applicants who used over 1.4 million potentially stolen or invalid Social Security numbers (SSNs).

Magnifying glass looking at money on a blue background

2. Fraud Prevention Alert: Cross-agency data analytics can prevent fraud in government programs.

We compared data of applicants who received benefits from the Department of Housing and Urban Development and also qualified for PPP loans from the Small Business Administration and found that some may have deliberately misrepresented their incomes.

3. Case Study: We assisted the PBGC OIG in identifying misrepresentations in pension plan assistance applications.

We developed a predictive risk model to help the Pension Benefit Guaranty Corporation (PBGC) OIG identify overpayments of Special Financial Assistance funds. This risk model provided support to PBGC and the Department of Justice recover $262 million related to improper payments.

image of social security card, stacks or money and bar chart

4. Fraud Alert: Improved government information-sharing and controls would strengthen program integrity.

We identified 69,000 questionable SSNs used to obtain $5.4 billion in potentially fraudulent pandemic loans from the Small Business Administration’s (SBA) Paycheck Protection Program and Economic Injury Disaster Loan program.

data icons with money and social security cards

5. Fraud Alert Follow-Up: Our data analytics tools combined with the Do Not Pay system could prevent and detect fraud beyond the pandemic.

In this Fraud Alert Follow-Up, we found $38 million in potentially improper or fraudulent loans obtained using deceased people’s SSNs. The results reveal potential benefits for agencies to continue to use the Department of the Treasury’s (Treasury) Do Not Pay system to strengthen program integrity.

6. Case Study: A risk scoring model rates Coronavirus Relief Fund spending.

We created a risk-scoring model to streamline Treasury OIG’s review process for the Coronavirus Relief Fund (CRF). Our advanced data analytics techniques helped Treasury OIG prioritize fraud investigations and audit reviews related to CRF prime and sub-recipients.

We continue to use advanced data analytics to support ongoing pandemic investigations for Offices of Inspectors General and law enforcement agencies, with a focus on identifying potential organized fraud schemes, often resulting in a higher number of subjects and increased financial recoveries.

-Ken Dieffenbach, PRAC Executive Director

Page last modified: 04/06/2026

PRAC Advanced Data Analytics

Data Analytics by the Numbers

Analytics Highlights

1. Fraud Prevention Alert: Pre-Award Vetting Could Have Prevented Over $79B in Potentially Fraudulent Payments

2. Fraud Prevention Alert: Cross-agency data analytics can prevent fraud in government programs.

3. Case Study: We assisted the PBGC OIG in identifying misrepresentations in pension plan assistance applications.

4. Fraud Alert: Improved government information-sharing and controls would strengthen program integrity.

5. Fraud Alert Follow-Up: Our data analytics tools combined with the Do Not Pay system could prevent and detect fraud beyond the pandemic.

6. Case Study: A risk scoring model rates Coronavirus Relief Fund spending.

Glossary

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PRAC Advanced Data Analytics

Data Analytics by the Numbers

Analytics Highlights

1. Fraud Prevention Alert: Pre-Award Vetting Could Have Prevented Over $79B in Potentially Fraudulent Payments

2. Fraud Prevention Alert: Cross-agency data analytics can prevent fraud in government programs.

3. Case Study: We assisted the PBGC OIG in identifying misrepresentations in pension plan assistance applications.

4. Fraud Alert: Improved government information-sharing and controls would strengthen program integrity.

5. Fraud Alert Follow-Up: Our data analytics tools combined with the Do Not Pay system could prevent and detect fraud beyond the pandemic.

6. Case Study: A risk scoring model rates Coronavirus Relief Fund spending.

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