Policy Impact Analysis - 117/HR/8322

Bill Overview

Title: STOP Fraud Act

Description: This bill addresses issues of fraud and improper payments, including by establishing the Federal Real Antifraud Unified Directorate within the Office of Management and Budget (OMB). The bill requires agencies to designate any program exceeding certain payments thresholds as a program susceptible to significant improper payments and to implement proactive analytics for a high-risk area of each designated program. The OMB must designate any program with outlays equal to or in excess of $50 billion with respect to the preceding fiscal year as a high-priority program. An agency administering a high-priority program must develop a plan to implement anti-fraud controls that include digital identity-proofing solutions, threat intelligence, and proactive analytics. Such plan must take into consideration the administrative burden of implementing such anti-fraud controls. The bill establishes in the Treasury a Program Integrity Fund. The bill modifies improper payments provisions, including by requiring compliance reports by inspectors general of executive agencies at least every three fiscal years (currently, annually).

Sponsors: Rep. Connolly, Gerald E. [D-VA-11]

Target Audience

Population: People interacting with significant federal expenditure programs susceptible to fraud

Estimated Size: 100000000

Reasoning

Simulated Interviews

Social Security Benefits Recipient (California)

Age: 45 | Gender: female

Wellbeing Before Policy: 6

Duration of Impact: 5.0 years

Commonness: 15/20

Statement of Opinion:

  • I hope this policy will make the process smoother and reduce fraud, but I worry about additional paperwork.

Wellbeing Over Time (With vs Without Policy)

Year With Policy Without Policy
Year 1 6 6
Year 2 7 6
Year 3 7 6
Year 5 8 5
Year 10 6 5
Year 20 6 4

Unemployment Insurance Claimant (Texas)

Age: 30 | Gender: male

Wellbeing Before Policy: 5

Duration of Impact: 3.0 years

Commonness: 10/20

Statement of Opinion:

  • I am concerned that increased verification process might delay needed funds.

Wellbeing Over Time (With vs Without Policy)

Year With Policy Without Policy
Year 1 5 5
Year 2 5 5
Year 3 6 5
Year 5 6 5
Year 10 5 4
Year 20 4 3

Medicare Beneficiary (Florida)

Age: 62 | Gender: male

Wellbeing Before Policy: 7

Duration of Impact: 10.0 years

Commonness: 15/20

Statement of Opinion:

  • If the policy safeguards Medicare, it's good, but I hope it doesn't lead to service cuts.

Wellbeing Over Time (With vs Without Policy)

Year With Policy Without Policy
Year 1 7 7
Year 2 7 7
Year 3 8 7
Year 5 8 6
Year 10 7 6
Year 20 6 5

Government Employee (New York)

Age: 55 | Gender: female

Wellbeing Before Policy: 8

Duration of Impact: 20.0 years

Commonness: 8/20

Statement of Opinion:

  • This could increase my workload, but it's necessary to prevent fraud.

Wellbeing Over Time (With vs Without Policy)

Year With Policy Without Policy
Year 1 7 8
Year 2 8 8
Year 3 8 8
Year 5 8 8
Year 10 8 8
Year 20 9 8

Data Analyst (Illinois)

Age: 40 | Gender: other

Wellbeing Before Policy: 6

Duration of Impact: 10.0 years

Commonness: 5/20

Statement of Opinion:

  • I believe this policy will lead to better use of data analytics to prevent fraud.

Wellbeing Over Time (With vs Without Policy)

Year With Policy Without Policy
Year 1 7 6
Year 2 8 6
Year 3 8 6
Year 5 8 6
Year 10 9 5
Year 20 8 5

Medicaid Service Provider (Ohio)

Age: 70 | Gender: male

Wellbeing Before Policy: 5

Duration of Impact: 5.0 years

Commonness: 7/20

Statement of Opinion:

  • The potential for payment delays worries me, but reducing fraud is crucial.

Wellbeing Over Time (With vs Without Policy)

Year With Policy Without Policy
Year 1 5 5
Year 2 5 5
Year 3 5 5
Year 5 6 4
Year 10 5 4
Year 20 5 3

IT Specialist (Nevada)

Age: 36 | Gender: female

Wellbeing Before Policy: 6

Duration of Impact: 20.0 years

Commonness: 5/20

Statement of Opinion:

  • This is an opportunity for growth in digital security fields.

Wellbeing Over Time (With vs Without Policy)

Year With Policy Without Policy
Year 1 7 6
Year 2 7 6
Year 3 8 6
Year 5 8 5
Year 10 8 4
Year 20 7 4

Veteran Affairs Beneficiary (Arizona)

Age: 52 | Gender: male

Wellbeing Before Policy: 7

Duration of Impact: 2.0 years

Commonness: 12/20

Statement of Opinion:

  • Enhancing the integrity of veteran benefits is necessary, but I hope it doesn't add complexity.

Wellbeing Over Time (With vs Without Policy)

Year With Policy Without Policy
Year 1 7 7
Year 2 7 6
Year 3 7 6
Year 5 6 5
Year 10 6 5
Year 20 5 4

Student Loan Recipient (Colorado)

Age: 28 | Gender: female

Wellbeing Before Policy: 5

Duration of Impact: 5.0 years

Commonness: 12/20

Statement of Opinion:

  • Increased fraud detection might protect loans, but I hope it doesn't complicate the process.

Wellbeing Over Time (With vs Without Policy)

Year With Policy Without Policy
Year 1 5 5
Year 2 6 5
Year 3 6 5
Year 5 6 5
Year 10 5 4
Year 20 4 3

Federal Inspector General (Washington)

Age: 68 | Gender: other

Wellbeing Before Policy: 8

Duration of Impact: 10.0 years

Commonness: 5/20

Statement of Opinion:

  • This change is timely. It will aid in identifying and rectifying systemic fraud.

Wellbeing Over Time (With vs Without Policy)

Year With Policy Without Policy
Year 1 8 8
Year 2 8 8
Year 3 8 8
Year 5 9 8
Year 10 9 8
Year 20 8 7

Cost Estimates

Year 1: $250000000 (Low: $200000000, High: $300000000)

Year 2: $225000000 (Low: $180000000, High: $270000000)

Year 3: $225000000 (Low: $180000000, High: $270000000)

Year 5: $200000000 (Low: $160000000, High: $240000000)

Year 10: $150000000 (Low: $120000000, High: $180000000)

Year 100: $100000000 (Low: $80000000, High: $120000000)

Key Considerations