R ecovery Accountability & Transparency Board Recovery Operations Center 2010 National Fraud Awareness Conference 28 July 2010 Douglas R. Hassebrock

2009 – $410 Billion dollars later….. Presented overall concept of managing referrals, open-source research, data exploitation, and predictive modeling Operations center stood up in Nov 2009 Over 2000 complaints, 160 leads, over 400 Requests for Assistance Recovery Oversight Overview

A WARD A MOUNTS VS. A MOUNT E XPENDED T OP 10 F EDERAL A GENCIES DOE stands out as the agency with the greatest variance between amount awarded and amount expended. * Percentages based on the amount awarded or expended compared to the total amount awarded or total amount expended, respectively. 3

Module Accountability 2009 Concept

2010 Recovery Operations Center

Recovery Operations Center Initial Risk Screening All recipients are put through a risk model using variables that are constantly being monitored for relevancy

Stage II - Target Analysis An in-depth fraud analysis capability utilizing the vast amount of public information about companies receiving Recovery Act funds in order to identify non-obvious relationships between legal entities. These relationships will unveil facts that may not have been transparent to government officials at the time of contract or grant award. These relationships might result in leads for investigation, leads for audits, identifying an added risk factor, or identifying excluded parties receiving Recovery Act funds.

Recovery.gov recipient and agency reporting information Central Contractor Registration (CCR) Online Representations and Certifications Application (ORCA) Excluded Parties List System (EPLS) Federal Procurement Data System (FPDS) USASpending.gov Over 11 million risk relevant global public records (RDC - 5,000 new records added every day; over 270 global watch lists reviewed daily) Federal Assistance Award Data System (FAADS) Accurint/LexisNexis RATB internally-generated procurement issues and observations Dun & Bradstreet Corporate Hierarchy Data Public Access to Court Electronic Records (PACER) Open Source Data Current

RATB Fraud Hotline complaint information Recovery IG complaint information FinCEN Other data fusion centers – perhaps linking together with yours? Example Analysis Restricted Data Updates

Accountability Module Information Flow

Stage III – Using Risk to Focus Limited Resources We’re looking at multiple risk factors to determine the most susceptible areas of fraud or taxpayer waste. Those areas will be determined by predictive variables like high-risk programs, high- dollar-value projects, past criminal history in projects or regions, tips from citizens, etc.

U.S. Government-Wide Recovery Contract Spending Darker blue indicates higher spending in the county; lighter blue indicates less.

U.S. Government-Wide Recovery Grant and Loan Spending Darker green indicates higher spending in the county; lighter green indicates less.

U.S. Government-Wide Recovery High Risk Program* Grant and Loan Spending Top Counties Darker green indicates higher spending in the county; lighter green indicates less. *High risk programs are as identified by the Inspectors General.

U.S. Government-Wide High Risk Recovery Spending, Risk Factors and RATB Complaints Darker red indicates a higher risk county; lighter red indicates a lower risk. The riskiness of a county comes from the total quantity of spending in that county and its level of risk from the risk model.

The ROC is positioned well to serve as an centralized intelligence hub for information sharing. From preliminary analysis of OIGs providing complaint data, we found…. Targets being worked by multiple IGs Targets being worked by multiple IGs in the same zip code. Considering our analysis platform, imagine of we shared data across all IGs? –How could we be operating more efficiently? Information Sharing…possibilities

The Board is legislated to terminate in 2013 The data will only get better and better and more relevant; –Centralized hub capability –Decentralized analysis –Ability to segregate if needed…. Recovery Operations Center – Future

Recovery Operations Center “Analysis 2.0” Data Matching Target Lists Risk Models (always improving) INPUTSYNTHESISOUTPUT Screening OIG Offices Fusion Centers? Analysis Risk Mapping Referrals to IGs Results flow back to screening Resource Mgt RFAs Hotline

The tools being used now on Recovery data have been used successfully in other government and private companies to identify criminal trends and reduce fraudulent activity. Prior to now, the IG community has never had the opportunity to apply this technology to a singular act across multiple federal programs. Recovery Operations Center will provide more targeted oversight to a single funding effort than ever before in government. Summary

We’re finding that even with the most advanced approach to isolating risk, the end action must be “boots on the ground.” We believe that using analysis to focus resources is a sound methodology, and better suited towards prevention than simply allowing for the phone to ring from a hotline; However, the real answers to questions posed by analysis can only be discovered by auditors, investigators, and inspectors that act upon those leads. Closing