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FBO DAILY - FEDBIZOPPS ISSUE OF JANUARY 06, 2018 FBO #5888
SOLICITATION NOTICE

B -- Federal Crop Insurance Program Spot Check List Study - RFQ

Notice Date

1/4/2018
 

Notice Type

Combined Synopsis/Solicitation
 

NAICS

611310 — Colleges, Universities, and Professional Schools
 

Contracting Office

Department of Agriculture, Farm Service Agency, Acqusition Management Division Kansas City Acquisition Branch, P. O. Box 419205, Kansas City, Missouri, 64141-6205, United States
 

ZIP Code

64141-6205
 

Solicitation Number

RMASLC010418
 

Point of Contact

Julie M. Simpson, Phone: 8169261200
 

E-Mail Address

julie.simpson@kcc.usda.gov
(julie.simpson@kcc.usda.gov)
 

Small Business Set-Aside

N/A
 

Description

RFQ - please read document in its entirety. The Agricultural Risk Protection Act of 2000 amended the Federal Crop Insurance Act to direct the Secretary of Agriculture to use data mining and related technologies to ensure the integrity of the Federal crop insurance program. The U.S. Department of Agriculture's Risk Management Agency (RMA), which oversees the Federal crop insurance program, subsequently partnered with the Center for Agribusiness Excellence (CAE) at Tarleton State University to develop and administer a data mining and warehousing program as authorized by the Act. RMA, working with CAE, now makes extensive use of data mining products to enhance the integrity of the Federal crop insurance program. Data mining is used to detect and deter fraud, waste and abuse by identifying producers, insurance agents and loss adjusters whose insurance claim outcomes are anomalous as compared to their peers. The Spot Check List is the most well-known, comprehensive, and routine application of data mining to the Federal crop insurance program. Annually, RMA uses objective, data-driven criteria to develop a list of producers whose loss experience is anomalous relative to similarly situated producers in the geographic area. Research and observations gathered from RMA field staff and approved insurance provider (AIP) personnel are used to identify "schemes" that have been used to exploit the crop insurance program through fraudulent or abusive insurance claims. The claimed losses may be due to a structured scheme (e.g., yield switching), or may simply reflect a pattern of unusually high loss ratios, loss frequencies, and/or severe losses relative to peers. Data mining algorithms are developed based on the schemes and are used to identify policies with anomalous loss experience. Loss comparisons are by local geographic area and for the same crop, type, and practice. The policies so identified are placed on the annual SCL. In recent years, the number of producers placed on the SCL has typically been around 1,700, and the policy count about 3,000. It is important to note that being placed on the SCL does not mean a producer has engaged in fraudulent or abusive activities. Rather, it means only the experience of a producer on the SCL is not consistent with that of peers and warrants further scrutiny. The producers placed on the SCL are notified their farming operations are subject to additional monitoring and inspections during the growing season to ensure that claims for losses are legitimate, and to serve as a deterrent to potentially fraudulent or abusive activities. From 2001 to 2011, the USDA Farm Service Agency had sole responsibility for conducting the SCL reviews. The FSA review consists of growing season and pre-harvest inspections to assess whether the condition of the insured crop is consistent with that of other producers in the area. Beginning with 2012 the AIPs assumed responsibility for a portion of the SCL reviews in order to make more effective use of available government and AIP resources. The AIP review is more comprehensive than that of FSA, and consists of a full policy review, of which in-field inspections are but one component. The allocation of policies between FSA and the AIPs is at RMA's discretion and can change based on workload, changes in other program requirements, etc. Currently, SCL policies are split approximately 50-50 between AIPs and FSA. In general, the assignment of individual SCL policies to the AIPs or FSA is random so as to avoid potential systematic biases, i.e., a policy has an equal probability of being assigned to an AIP or to FSA. The impact of the SCL as a means of fraud/abuse mitigation is measured as cost avoidance, the amount by which indemnity payments to producers on the SCL decline after being placed on the list. From 2001 through 2015 the cumulative cost avoidance is estimated to be $1.16 billion. Annual cost avoidance estimates are shown in the table below. Estimated Cost Avoidance Attributed to Spot Check List, 2001-2015 ($ Million) 2001 $48 2009 $89 2002 $112 2010 $112 2003 $81 2011 $46 2004 $71 2012 $91 2005 $140 2013 $70 2006 $27 2014 $54 2007 $85 2015 $61 2008 $73 2001-15 cumulative $1,160 These cost avoidance estimates rely on before-after comparisons of indemnities paid to producers on the SCL. While there have been some refinements over the years (e.g., to account for changes in prices), the basic approach remains unchanged. RMA is concerned the methodology may not adequately control or account for other factors potentially relevant to measuring the impact of the SCL. For example, differences in growing conditions between years will clearly have a significant impact on changes in indemnity payments through time. Also, it is possible that the reduction in indemnities may reflect some degree of mean-reversion for producers on the SCL, i.e., a natural convergence to long-run loss expectations. Other potential confounding factors include the differences in the inherent riskiness of land farmed by producers on the SCL relative to non-SCL producers, the impact of micro-climates and localized weather events, and a variety of other environmental influences that may impact expected loss frequencies and/or magnitudes. Another concern with the current cost avoidance measure is that it does not consider monetary impacts beyond the initial year the producer is placed on the SCL, though the effects presumably extend beyond one year. In addition, the current measure does not consider the possible deterrent effect on the neighbors of those placed on the SCL, individuals who may have otherwise been tempted to "push the limits". An additional complication is that the impact of SCL reviews conducted by AIPs may be different from those conducted by FSA. Because of considerations as described above, RMA believes a more rigorous evaluation of the SCL and the cost avoidance methodology is needed to better assess the impact of the SCL as a means to enhance program integrity. Federal crop insurance is routinely identified as the linchpin of the farm safety net by producers, and assuring public support is critical to the sustainability of the program. Fraudulent or abusive activities not only waste scarce taxpayer resources, but also undermine public confidence in the program. Thus, RMA is constantly seeking ways to reduce or eliminate program vulnerabilities while providing responsive, valuable risk protection to producers. The SCL is a key element of RMA's overall strategy to combat fraud, waste and abuse in the federal crop insurance program. As such, it is imperative that RMA fully understand the monetary and behavioral impacts of the SCL as a means to ensure program integrity. Results from research will help RMA to better measure the impacts of the SCL, improve the SCL process through refinement of data mining algorithms and enhance the reviews conducted thereunder, deploy available government (RMA, FSA) and AIP resources more appropriately, and strengthen the integrity of the Federal crop insurance program.
 

Web Link

FBO.gov Permalink
(https://www.fbo.gov/spg/USDA/FSA/KCAO/RMASLC010418/listing.html)
 

Place of Performance

Address: 1400 Independence Avenue, Washington, District of Columbia, 20250, United States

Zip Code: 20250
 

Record

SN04781095-W 20180106/180104230652-cba39e542a62b436ce6747a24f9e1221 (fbodaily.com)
 

Source

FedBizOpps Link to This Notice
(may not be valid after Archive Date)

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