CINCS In Focus. Detecting and Preventing Fraud in the US Federal Crop Insurance Program

Transcription

1 CINCS In Focus Detecting and Preventing Fraud in the US Federal Crop Insurance Program 19th December, 2012

2 Mitigating the Risks of Fraud in the USDA Federal Crop Insurance Program By CINCS Team Member Steven Shonts Overview Record setting drought and heat during the summer of 2012 decimated corn and soy crops across the Midwest, producing billions in economic losses to farmers across the region. Thankfully the majority of these losses, an estimated $20 billion, 1 were covered by the Federal Crop Insurance Program, administered by the Risk Management Agency (RMA) of the United States Department of Agriculture (USDA). By transferring risks from individual farmers to insurance companies and the government, crop insurance has enhanced the stability of American agricultural production, and offered a safety net to America s farmers. But while the benefits of the program are substantial, many have recently highlighted the potential for fraudulent claims in the program, with estimates reaching into the billions. By centralizing and standardizing fraud detection and investigation methods across RMA offices, and enhancing collaboration and data sharing between RMA, other US government agencies, and insurance companies, the USDA could greatly minimize the potential for fraud and save the taxpayers significant money. Introduction The Multi-Peril Crop Insurance policies covering American farmers trace their origins to the great depression and dust bowl of the 1930s, when many farmers suffered bankruptcy and were forced to flee America s breadbasket due to several consecutive years of damaging weather 2. Since its inception, the program has grown to cover approximately $80 billion of crops spread across 281 million acres (114 million ha) of US farmland 3, with over 80% of staple crops (corn, soybeans, wheat, cotton, sorghum, barley, rice, potatoes, tobacco, and peanuts) covered 4. Farmers purchase protection through one of 16 federally licensed private insurance companies and pay premiums similar to conventional insurance policies, though these premiums are substantially subsidized through the RMA to incentivize participation. Due to these subsidies, while total premiums paid into the program last year were $13.06 billion, 1 UDSA RMA (October 2012) Financial Report Fiscal Year 2012: Management s Discussion and Analysis 2 National Crop Insurance Services (2012) About Crop Insurance 3 USDA Risk Management Agency (2010). About the Risk Management Agency 4 USDA Risk Management Agency (2011) Strategic Plan

3 taxpayers subsidized $7.15 billion 5 of this amount (for a complete breakdown of taxpayer and farmer premiums for the ten years leading up to 2012, see figure 1 below). The fact that taxpayers ultimately backstop program losses and provide large subsidies from government coffers highlights the need for stricter accountability and auditability in the program. Fraud has long been a documented issue with crop insurance 6,7 and is exacerbated by the difficulty of documenting and investigating farmers claims. The RMA s own Strategic Management Plan estimates that upwards of 5% of claims paid under the program may be fraudulent, and has the goal to reduce this number by a percentage point by However the General Accountability Office (GAO) and other government auditors have published reviews claiming more can be done to detect and deter fraudulent activity. The GAO in particular highlights some shortcomings in the RMA s internal data management system which, if rectified, could significantly improve claims management. Figure 1 Premium income and indemnity payments under the Federal Crop Insurance Program for the past 10 years This report begins with an overview of the current data management architecture used by the RMA and related agencies, then offers a series of suggestions for the agency to improve upon these measures by harnessing emerging cloud GIS technologies. 5 USDA Risk Management Agency (2012). Fiscal Year Costs : Premium Breakout 6 NPR (11/15/2005) Crop Insurance Program Ripe for Fraud 7 CNBC (6/21/2012) Crop Insurance Set to Expand Despite Growing Fraud Worries 8 Ibid. 4

4 Current Use of GIS and remote sensing within RMA RMA already uses Geographic Information Systems (GIS) technology internally for originating, monitoring and paying out claims, including the construction and sharing of actuarial maps. The agency uses ESRI s ArcGIS Server for information sharing between its 10 regional offices, in order to disseminate information used to price policies and determine different risk classes 9. It further uses this system for publishing actuarial maps for insurance providers, though this linkage is primarily one-way (there are not extensive pathways to allow data collected by insurance providers to enter the RMA data store). The agency also has access to the extensive collection of remote sensing imagery (including landsat 5 and 7) from the USDA imagery archive. 10 Despite this existing infrastructure, the RMA strategic plan 11 explicitly states a desire to increase the use of Geographic Information Systems (GIS) remote sensing and data mining technologies to more accurately and efficiently manage RMA s programs to enhance delivery of products. The agency also uses GIS and remote sensing analysis to monitor potentially fraudulent activity and investigate claims. The agency states that advance data mining techniques have saved taxpayers some $730m in fraud in the decade ending in Harnessing its data store, including remote sensing imagery, RMA uses different geospatial analytics to identify potential areas of vulnerability and performs targeted reviews (ex ante as well as ex post) to address identified areas of known or potentially high vulnerability. The main initiative through which the agency acts on suspicion of fraud is known as the spot list. The RMA, in conjunction with the USDA s Farm Service Agency (FSA), sends out notifications to a selected subset of covered farms every year that have been identified as making anomalous claims under the program. Reduced claims from these spot list policy holders in subsequent years is calculated to have saved the program over $800 million, 13 indicating that many of the farmers placed on the spot list significantly modified their behavior due to their selection. The extent to which farmers making fraudulent claims are placed on the spot list from year to year consistently, however, remains unclear. The RMA also uses satellite imagery to perform forensic remote sensing to follow up on suspected cases of fraud. This involves attempting to compare the birds-eye observations with the farmer s reported management plans using 16-day LANDSAT 5 and 7 imagery from the USDA imagery archive 14. The agency has recently stepped up efforts to train in-house staff to 9 ESRI (2011) GIS for Agribusiness: Insuring America s Farmland USDA Risk Management Agency (2011) Compliance Report 11 Ibid LA Times (2/6/2011) Farm Insurance Fraud is Cheating Taxpayers Out of Millions 13 US Government Accountability Office (2012) Crop Insurance: Savings Would Result from Program Changes and Greater Use of Data Mining 14 Ibid. 9

5 perform such analyses, though most of these cases still require verification by 3 rd party contractors 15. Despite these efforts, the US General Accountability Office (GAO), a government watchdog agency, recently claimed in a report to congress that RMA and FSA have not taken full advantage of data management techniques to increase the effectiveness of data mining [ ] The value of identifying farmers with anomalous claim payments may be undermined by the fact that FSA does not complete all field inspections and neither FSA nor RMA has a process to ensure that the results of all completed inspections are accurately reported, in accordance with USDA s written procedures. 16 In fact, upwards of 28% of claimants notified of being placed on this list did not actually have subsequent claims followed up on. This is likely the result of a currently fragmented division of responsibilities between the RMA, the FSA, regional field offices and the private insurance companies, which hampers robust communication. Additionally, the lack of harmonized processes for detecting and investigating claims of fraud, including data custody issues, prevents optimal collaboration for catching fraud. Opportunities to strengthen RMA s internal data architecture By implementing ArcGIS Server and creating a system to share geographic data with regional offices, RMA has already taken a big step in the right direction in terms of data management practice. However, improvements could likely be made in terms of 1) increasing the use and accessibility of the system to collect and disseminate more complete information, and 2) creating more standardized procedures for fraud detection and investigation. System use and accessibility Since the RMA already has a central storehouse for claims information, the central challenge here is to increase its use and accessibility to staff both within RMA and at associated agencies, including the FSA and the private insurers ultimately responsible for administering the program. According to the above-referenced GAO report, the FSA and the RMA often have mis-aligned incentives and as a result often fail to coordinate activities in an efficient way. The insurance companies are even more out of the loop. Expanding the scope and accessibility of the central database could greatly assist in keeping all these disparate agencies on the same page. The GAO note that RMA and FSA have a complicated process for transmitting data [from FSA field inspections], creating opportunities for errors and omissions staff in about 1,000 FSA county offices transmit their field inspection data to nearly 50 state offices by ing data, mailing CDs or paper documents, or inputting the data in their FSA computer systems. 17 They further report that this has resulted in improprieties in the program going undetected: In 2009, the Inspector General reported on two farmers on the list of farmers with anomalous claim payments whose crops were in good condition, according to the FSA inspection; however, these farmers filed nearly $300,000 in claims a short time after the FSA inspection, and RMA did not notice the discrepancy. 15 Ibid Ibid Ibid. 13

6 Technologically, the main issue preventing better collaborating is that these different parties all have different IT management systems which are server or mainframe-based. Enhancing the web service capabilities of the RMA system and hosting a portion of the related data on a geospatial cloud infrastructure could very well help bridge this gap, allowing all stakeholders efficient access to information More standardized procedures - Another way to enhance fraud detection and investigation within the RMA would be to introduce more standardized procedures for claims monitoring and evidence gathering. The above-cited GAO report revealed significant issues related to the methodology FSA used to determine its improper payment estimates, including selected sample payments that were not reviewed, calculations that did not account for program payment variables, missing supporting payment documentation, and substantial eligibility criteria that were not considered. 18 Part of the reason for this is that much of the current analysis is conducted by the RMA regional offices, who each have different criteria, evidence gathering procedures and in-house technical capacity for fraud monitoring. A central fraud tracking system could allow better oversight and transparency in data mining and remote sensing interpretation, and allow the sharing of expertise between different regional offices with differing technical strengths. It could also be used to build quality control tools for remote sensing interpretation and analysis. The GAO report also notes that the RMA does not have documented agency policies to follow up on anomalous or suspicious claims, which may allow many fraudulent activities to go undetected. They recommend building off Standards for Internal Control in the Federal Government 19 to create a better management chain for documentation and a standard set of procedures to follow in cases of anonymous tips reaching the agency. New technologies to better detect and investigate fraud In addition to improving internal data management capabilities, the RMA could benefit from configuring several workflow management techniques to harness advanced modeling and new innovations in cloud access to GIS infrastructure. Similar to our suggestion that RMA do a better job standardizing the processes by which they flag and follow up on suspicious activity, the RMA could further develop some modeling applications that would allow automatic processes to determine suspicious activities. The most obvious to incorporate would be biometric models of crop yield as a function of soil type and climate data, both of which are already collected by the USDA. These predictive models could quickly allow RMA staff to determine areas most likely to claim indemnity payments, allowing quick mobilization of claims adjustors and other resources. The models could also easily be compared with reports and claims coming from farmers in real time, highlighting areas or fields 18 USDA Office of the Inspector General (2007) Semiannual report to Congress FY nd Half 19 US General Accountability Office Internal Control: Standards for Internal Control in the Federal Government

7 with significant discrepancies to follow up on with more in depth investigation. These could eventually yield a standardized set of thresholds for important metrics which would trigger an automatic investigation, and would help bring the RMA in line with government best practice as described above. Further, as more data is collected the models themselves can be better calibrated to enhance their predictive power, leading to better-priced premiums and even helping FSA to provide advice to farmers on best management and cropping practices. Another beneficial expansion to the current RMA GIS infrastructure would be a clientfacing web service linked to the current GIS database. Farmers could be prompted to enter their management plans and activities through a web browser, in doing so establishing a defensible and auditable record of farm management. In the case of fraud, this could be compared with remote sensing and other data to support or refute the farmer s claim. Since this data would be collected during the course of the year, this would serve to discourage fraud in the first place since a farmer would need to pro-actively falsify management activities and enter these into the system, creating a clear paper trail of the wrongdoing. These records could then be crossreferenced with remote sensing data and climatic data in real time, to alert officials to inconsistencies between reported activities and observations. Conclusion While the RMA has recently made fraud prevention and detection a policy priority, there is still significant room for improvement. By creating links with data collected by other USDA agencies, as well as insurance providers, the RMA could gain deeper insights through its data mining activities. Additionally, by creating standardized workflows and applications, it could allow its staff to harness the power of these technologies, which are only currently available to those familiar with GIS and remote sensing. Finally, by embracing cloud delivery the agency can develop a suite of user-friendly tools and workflows to better monitor and investigate claims and prevent fraudulent activities before they happen. The progress made by USDA in harnessing GIS over the past 10 years is laudable, but developing technologies continue to offer a strong value proposition for better serving both farmers and taxpayers.