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.

Answer 1

· The case was about . Fraud detection Child care velfare Use data mining solution The various components are . Fraud referrals ver payements Share of cost Temination or reduction of benefits 2. It was designed to address . New fraud referrals Fraud is detected early To increase the efficiency Detection of suspicious participants 3. It is very important because Saves the child Done by earlier detection of child care providers who aresuspicious Prevents the country by fighting fraud Saves millions of tax payers 4. The data mining tools that may be used are like SAS business intelligent tools i. Creates infomation portal SAS fraud framework i. Performs forecasting ii. Predictive analysis iii. Social network analysis 5. Data source Historical [2001] i. GEARS

ii. LEADER case ii. Child care files liscensimg iv. Data from fraudcase . External source i. IHSS ii. IEVs ii NHR iv. Social network data 6. Done by Predictive model Peer group analysis . Patterns of fraudulent activities . It has been detected earlier Reduces duration of fraudulent Welfare fraud detection and investigation .