Uncovering social service fraud saves millions, reinforces public trust Los Ange
ID: 3813422 • Letter: U
Question
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. Instant access to this network of child care recipients and providers saved fraud investigators innumerable hours of casework preparation. “It would take me months or years to uncover all of the relations shown,” said one investigator. “On one of my cases, with a single click of my mouse, I saw leads to additional evidence that would have otherwise taken weeks, possibly months to uncover. This included evidence such as addresses and names of potential unreported employers and potential second-residence addresses. The system also showed a connection between my suspect and two other suspects on two other cases.” Also, one investigator, who was nearing the conclusion of a 10-person conspiracy investigation, ran the main suspect's name through the social network analysis and discovered seven potential additional co-conspirators that she would not have otherwise discovered. Strong return on investment, invaluable outcomes. From May 2011 through May 2015, the following actions were initiated: 88 cases were referred to the district attorney for felony prosecution. 941 DMS fraud referrals were initiated for investigation. 1,538 referrals to DPSS case workers were submitted for follow-up action, of which 879 have resulted in one or more of the following: o Fraud referrals for reasons other than child care fraud. o Denial/termination/reduction of various public assistance benefits. o Overpayments. o Medical share of cost. Read the uploaded case and answer the following questions: 1.)What is the case about? Give the various components that make up the case. 2.)What was the problem to be solved in this case? 3.)Why was solving this problem very important? 4.)What Data Mining Tool was deployed to solve this problem? 5.)What data sources were used? 6.)How did the DM Tool find fraud patterns? 7.)How did the DM Tool unravel conspiracies and empower investigators.
Explanation / Answer
Solution:-
1) This case is about that the LA county government is supporting the eligible communities by running the many health care schemes, employment schemes, finencial services and child care schemes. And government has also need to stop the fraudulent activities which exploits the schemes. For this purpose LA County government turned to SAS data mining tools and other technologies to find the frauds and stop them. So that this will result in empowerment of the needy communities, trust of the people and saving the valuable money of the taxpayers.
The important components discussed are as given below-
a) The LA county government and Department of Public social Services DPSS.
b) The SAS data mining tools and technologies
c) The data sources to find fraudulent patterns
d) Various public empowerment schemes
e) Fraudulent members of the society which are to be stopped.
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2) The problem of the frauds is major concern of this case. The DPSS and running many schemes o empowerment of the eligible communitie but some fraudulent people exploits the service. They show false needs, falsely declare that their children are attending nonexistent child care centers and claims the money as per the scheme. So this the problem to be solved here.
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3) This problem decrease the trust of the people if the government is unable to handle this issue. This the the wastage of the valuable money of the taxpayers and the fraudulent people doesn't have need but they exploits the schemes. But the needy communities should be actually get benefits.
So this is very important to solve this problem to empower the eligible communities not the fraudulent people and make value to the money of taxpayers. This will increase the trust of people in the government.
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4) Here to solve this problem the SAS data mining tools are used. The SAS data mining solution need to be designed to find the fraudulent activities. The government use portal which stores the found frauds and their history. Analyze the social networks to find the patterns of frauds . By analysis the investigator can found the fraudulent rings and their pattern of contacts. By data mining and analysis the predictive model can be designed so it is quite possible to predict the frauds.
So these are some tools and data mining technologies which are used to solve this problem.
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5) The data sources are social networks of the fraud people. In the social networks this can be find the rings of the frauds. Social network analysis also helped identify collusive fraud rings in companion cases. So the data sources are the social networks.
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6) The data mining tools work on the data collected by the data sources. The data mining and the analysis of the social networks the fraudulent rings and collusive connection can be notified. By social networks analysis the predictive model is designed and by it can be predicted that which poeple can be frauds. So this is possible for DMS to detect conspiracy groups much earlier, significantly reducing the duration of fraudulent activities.
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7) The Data Mining tools are support the investigator to find the frauds rings exists. First the Data mining technologies helping by analyze the data collected by social networks and identity the fraudulent activity and patterns. And secondly there portals are used by the government on which the information of previous frauds and fraudulent people is stored. So the investigator can observe the history of the fraudulent people and can identify them. Third the predictive model designed by the analysis of the social information, it helpsnthe investigator to predict the frauds and can stop them.
So these are the Data Mining tools which helps the investigators.
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