Homework Help is Here – Start Your Trial Now!
Business
Operations Management
Discussion Questions: What are some ways that data mining could be used to detect fraud in health insurance claims? 1. 2. How could private insurance companies and public government agencies collaborate to combat insurance fraud? 3. What types of business skills would be necessary to define the rules for and analyze the results from data mining? 4. What business processes are necessary to complement the IS component of data mining?

Discussion Questions: What are some ways that data mining could be used to detect fraud in health insurance claims? 1. 2. How could private insurance companies and public government agencies collaborate to combat insurance fraud? 3. What types of business skills would be necessary to define the rules for and analyze the results from data mining? 4. What business processes are necessary to complement the IS component of data mining?

Practical Management Science
Practical Management Science
6th Edition
ISBN:9781337406659
Author:WINSTON, Wayne L.
Publisher:WINSTON, Wayne L.
Chapter2: Introduction To Spreadsheet Modeling
Section: Chapter Questions
Problem 20P: Julie James is opening a lemonade stand. She believes the fixed cost per week of running the stand...
See similar textbooks
icon
Related questions
Question
Discussion Questions: 1. What are some ways that data mining could be used to detect fraud in health insurance claims? 2. How could private insurance companies and public government agencies collaborate to combat insurance fraud? What types of business skills would be necessary to define the rules for and analyze the results from data mining? What business processes are necessary to complement the IS component of data mining? 3. 4.
Transcribed Image Text:Discussion Questions: 1. What are some ways that data mining could be used to detect fraud in health insurance claims? 2. How could private insurance companies and public government agencies collaborate to combat insurance fraud? What types of business skills would be necessary to define the rules for and analyze the results from data mining? What business processes are necessary to complement the IS component of data mining? 3. 4.
1. Case Study #1: Combating Insurance Fraud with Data Mining and Analytics About 3% of the $2 trillion spent on health care in the United States is wasted on fraud every year. Insurers recover only a fraction of that $68 billion, but the information systems they use to spot suspicious claims are improving dramatically. Data mining and analytics are their most important weapons. They arm the insurer's special investigative units with information about potentially fraudulent billing patterns buried in millions of legitimate claims, spotting unusual trends that no human being working alone could ever see. Health Care Service Corp. (HCSC), for example, implemented a fraud detection system that drew on software from IBM and SAS, and it paid off almost immediately. An allergist in Illinois was submitting fraudulent bills, but the individual amounts were never high enough to trigger any suspicion. Something was amiss, however, and the system helped senior investigator, Mone Petsod, find it. Petsod drew a comparative analytics that showed what other allergists were charging for the same procedures. "It was so different" she said of the Illinois practitioner's billing history, and that finding helped uncover the $800,000 scam. Fraud detection systems rely partly on rules developed by human beings, and partly on patterns and trends that the analytic engines detect. A key step is to spot fraud before any claim is paid, because it is much easier to deny payment than to recover funds that have already been paid out. The time window is short, however, given pressures on payers to reimburse quickly. For example, Medicare acknowledges paying almost $50 billion per year in questionable claims, a figure that is much higher than the private sector's fraudulent claims rate. Government agents go after scammers once they see a pattern of abuse, but if the money is already paid, it is difficult to recover. Fraud detection systems, on the other hand, can operate quickly enough to catch suspicious claims before they are paid. Although data mining and analytics are potent weapons, it takes time for insurance company investigators to understand them and use them wisely. At HCSC, for example, the analysts must work closely with investigators to apply human judgment as they create new rules and follow data leads. Ôngoing training is essential, especially because the fraudsters continue to launch novel and increasingly complex schemes, changing their tactics to stay a step or two ahead. Knowing when, where, and how to drill down into the data to see meaningful patterns is a skill that agents must learn. But the investigators who master these skills will be able to combine their own experience and judgment with an immensely powerful information system to help reduce health care costs for everyone.
Transcribed Image Text:1. Case Study #1: Combating Insurance Fraud with Data Mining and Analytics About 3% of the $2 trillion spent on health care in the United States is wasted on fraud every year. Insurers recover only a fraction of that $68 billion, but the information systems they use to spot suspicious claims are improving dramatically. Data mining and analytics are their most important weapons. They arm the insurer's special investigative units with information about potentially fraudulent billing patterns buried in millions of legitimate claims, spotting unusual trends that no human being working alone could ever see. Health Care Service Corp. (HCSC), for example, implemented a fraud detection system that drew on software from IBM and SAS, and it paid off almost immediately. An allergist in Illinois was submitting fraudulent bills, but the individual amounts were never high enough to trigger any suspicion. Something was amiss, however, and the system helped senior investigator, Mone Petsod, find it. Petsod drew a comparative analytics that showed what other allergists were charging for the same procedures. "It was so different" she said of the Illinois practitioner's billing history, and that finding helped uncover the $800,000 scam. Fraud detection systems rely partly on rules developed by human beings, and partly on patterns and trends that the analytic engines detect. A key step is to spot fraud before any claim is paid, because it is much easier to deny payment than to recover funds that have already been paid out. The time window is short, however, given pressures on payers to reimburse quickly. For example, Medicare acknowledges paying almost $50 billion per year in questionable claims, a figure that is much higher than the private sector's fraudulent claims rate. Government agents go after scammers once they see a pattern of abuse, but if the money is already paid, it is difficult to recover. Fraud detection systems, on the other hand, can operate quickly enough to catch suspicious claims before they are paid. Although data mining and analytics are potent weapons, it takes time for insurance company investigators to understand them and use them wisely. At HCSC, for example, the analysts must work closely with investigators to apply human judgment as they create new rules and follow data leads. Ôngoing training is essential, especially because the fraudsters continue to launch novel and increasingly complex schemes, changing their tactics to stay a step or two ahead. Knowing when, where, and how to drill down into the data to see meaningful patterns is a skill that agents must learn. But the investigators who master these skills will be able to combine their own experience and judgment with an immensely powerful information system to help reduce health care costs for everyone.
Expert Solution
trending now

Trending now

This is a popular solution!

steps

Step by step

Solved in 2 steps

SEE SOLUTIONCheck out a sample Q&A here
Blurred answer
Recommended textbooks for you
Practical Management Science
Practical Management Science
Operations Management
ISBN:
9781337406659
Author:
WINSTON, Wayne L.
Publisher:
Cengage,
Operations Management
Operations Management
Operations Management
ISBN:
9781259667473
Author:
William J Stevenson
Publisher:
McGraw-Hill Education
Operations and Supply Chain Management (Mcgraw-hi…
Operations and Supply Chain Management (Mcgraw-hi…
Operations Management
ISBN:
9781259666100
Author:
F. Robert Jacobs, Richard B Chase
Publisher:
McGraw-Hill Education
Business in Action
Business in Action
Operations Management
ISBN:
9780135198100
Author:
BOVEE
Publisher:
PEARSON CO
Purchasing and Supply Chain Management
Purchasing and Supply Chain Management
Operations Management
ISBN:
9781285869681
Author:
Robert M. Monczka, Robert B. Handfield, Larry C. Giunipero, James L. Patterson
Publisher:
Cengage Learning
Production and Operations Analysis, Seventh Editi…
Production and Operations Analysis, Seventh Editi…
Operations Management
ISBN:
9781478623069
Author:
Steven Nahmias, Tava Lennon Olsen
Publisher:
Waveland Press, Inc.
SEE MORE TEXTBOOKS