2_use_case_name: Payment Redirect Fraud (PRF) Model
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: VBA: Veterans Benefits Administration
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: Criminals make direct deposit (DD) changes to steal Veterans’ benefit payments. Most direct deposit changes are safe, but 1-2 out of 1,000 (.1% - .2%) are fraudulent. The goal of the Payment Redirect Fraud (PRF) model is to identify which DD changes are likely to be fraudulent and refer them to team investigators for review and remediation.
12_outputs: The model identifies referrals (Veterans) with the highest scores indicating likely fraudulent activity. The outputs include Veteran PII and associated direct deposit bank account information.  These outputs are in the form of an R data file and an Excel file.
16_dev_stage: Operation and Maintenance
17_impact_type: Rights-Impacting

18_date_initiated: 10/1/2019
19_date_acq_dev_began: 12/1/2021
20_date_implemented: 10/21/2022
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: GS-00F-008DA
24_hisp_support: No
27_public_info: No
28_iqa_compliance: Results from the PRF model are for internal use only not shared outside of BDPR.
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: We use the population data from all historical fraud cases as well as model learning as a result of daily outputs.
34_data_docs: Documentation has been partially completed: Some documentation exists (detailing the composition and any statistical bias or measurement skew for training and evaluation purposes), but documentation took place within this use case’s development.
35_demo_features: Income, Age, Other
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: No
41_system_name: The PRF model does not run in a systems or a cloud, it runs by manual intervention on employee/contractor GFE machines
42_dev_tools_wait: More than 12 months
43_infra_provisioned: No
45_compute_request: Yes
47_timely_resources: No
49_existing_reuse: Re-use production level code and/or data products: This use case re-uses production level code as well as data products that have been published or made widely available for data science teams for within the agency.
50_internal_review: Limited documentation for review: Some information regarding the model’s performance on specific benchmarks are available or have been shared with specific stakeholders within the agency.
51_extension_request: No – Agency did not request an extension for this use case.
52_impact_assessment: Yes
53_real_world_testing: Performance evaluation in operational environment: The AI use case has been tested in an operational environment before being fully implemented as a solution, or has been tested in simulated or controlled environment using operational or synthetic data.
54_key_risks: False positives could result from the model output; however, this is mitigated by the fact that all referrals from the model output undergo a manual investigation by a fraud investigator.
55_independent_eval: TRUE
56_monitor_postdeploy: Automated and Regularly Scheduled Updates: Some aspects of the plan to monitor the AI system post-deployment are automated including re-training of models after detecting drift; however, data science teams are still significantly involved in the monitoring and re-deployment process.
57_autonomous_impact: No - Some individual decisions or actions require direct human oversight.
59_ai_notice: N/A - individuals are not interacting with the AI for this use case
61_adverse_impact: No
62_disparity_mitigation: The model does not incorporate race or gender into the model features.
63_stakeholder_consult: None of the above
65_appeal_process: No – it is not operationally practical to offer this.
66_no_appeal_reason: It is not practical to offer this because final decisions of fraud are based on human investigation and not solely on the model output.  The AI functionality is solely used to detect cases that are potentially fraudulent.  All cases are independently reviewed by fraud investigators to validate whether they are, in fact, fraud.
67_opt_out: No – This AI use case is not subject to the opt-out requirement because the AI functionality is solely used for the prevention, detection, and investigation of fraud or cybersecurity incidents, or the conduct of a criminal investigation.