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Uncovering social service fraud saves millions, reinforces public trust Los Angeles County uses SAS® to detect fraud, resulting in fewer losses, lower investigative costs and greater confidence from citizens In Los Angeles County, the Department of Public Social Services (DPSS) offers a range of programs to alleviate hardship and promote health, personal responsibility and economic independence. Across the county's many communities, DPSS offers temporary financial assistance, employment services, free/low-cost health insurance, food benefits, in-home supportive services for the elderly and disabled, and other financial assistance. To assist in program integrity efforts in the CalWORKs Stage 1 Child Care Program, LA County turned to SAS Analytics solutions to identify potential fraud, enhance investigations and prevent improper payments. A data mining pilot project revealed an 85 percent accuracy rate in detecting collusive fraud rings, with estimates of cost avoidance totaling $6.8 million. Convinced by the results, the county decided to move forward with implementing the Data Mining Solution (DMS) Application for the CalWORKs Stage 1 Child Care Program on May 2011. By proactively battling fraud, DPSS is helping the most vulnerable members of the community while protecting millions in taxpayer dollars. ® The system analyzes social networks to determine if individuals are likely to commit fraud. It also helps identify collusive fraud rings companion cases. Analyzing the data, finding the fraud patterns Fraud cases can include false employment claims where nonexistent employees are declared. In other cases, businesses are created by the heads of fraud rings who collude with recipients who falsely declare that their children are attending nonexistent child care centers. Sometimes, criminals declare work schedules that are false or shorter than the time amount claimed. To combat fraud, LA County first needed a data integration solution and a powerful analytical engine to bring together numerous internal and external data sources to build and run predictive models. With social network analysis and analytics, LA County can predict which benefit recipients and service providers are most likely to engage in fraudulent activity and create potentially large fund losses. Using predictive models and peer group analysis to detect anomalies in the use of child care services, LA County developed high-risk scores to decrease the number of false-positive cases assigned to investigators. The system uses a predictive model to analyze social networks and to assess the likelihood of child care fraud and collusion in fraud networks in the Child Care Program. The social network analysis also helped identify collusive fraud rings in companion cases. LA County uses SAS Fraud Framework for Government and incorporates SAS data mining technology with social network analysis, predictive analysis, rules management and forecasting techniques. SAS Business Intelligence has also been used to create an information portal where reports are housed and used to monitor and share information on fraud cases. By identifying historical patterns of fraudulent activity, investigators can focus on cases with a higher probability of ® ® fraud. These improved process efficiencies mean fraud investigators have more time to review highrisk cases. Unraveling conspiracies, empowering investigators SAS models have enabled DPSS' Welfare Fraud Prevention & Investigations (WFP&I) staff to identify and expedite the review of suspicious cases much earlier than waiting on referrals from contracted agencies or other referral sources. DMS detected conspiracy groups much earlier, significantly reducing the duration of fraudulent activities. LA County mapped out a network of participants and providers that visually displayed their relationships. They looked at whether any given small network fit into a larger scheme of networks, in which participants are in collusion with other child care providers. They identified strong central nodes and, in one case, found a child care provider serving many nodes of participants colluding in fraudulent activities. The aspect of the network that proved most valuable for fraud investigators was the social network analysis relationship display. This display shows a web of complex relations linked, for example, by common telephone numbers and addresses.