In January, the Fraud Section in the Criminal Division of the U.S. Department of Justice released its 2017 Year in Review. The report announced the launch of the Health Care Fraud Unit’s Data Analytics Team. The significance of this team is not to be underestimated: it represents a new level of sophistication in white-collar enforcement.
Data analytics has gained a foothold in law enforcement in predictive policing, where police use crime data to predict where crimes will occur and dispatch officers accordingly. Using data analytics in such a way not only enhances resource efficiency and improves response time, but also serves to reduce bias factors such as race and income. The launch of the Data Analytics Team at the Fraud Section exemplifies how data analytics can be applied beyond street crimes to complex white-collar cases.
During my time at the Fraud Section, I was part of a growing effort in using data as both evidence and an investigative tool. When prosecutors and I met with companies to review their compliance programs, we increasingly demanded data and measurements as evidence of these programs’ effectiveness. Similarly, we recognized how data could help in identifying trends and narrowing targets, and provide crucial frames of reference for the interpretation of evidence. For example, if a cardiologist is prescribing chemotherapy drugs at a volume that places him or her in the top 1 percent in the nation for those prescriptions, there is a good chance there is more than the practice of cardiology going on. Public health issues such as the opioid epidemic can be mapped in a way similar to street crimes, to guide the deployment of resources in enforcement responses.
To be sure, the use of data analytics in public health and civil enforcement is not entirely new. The Department of Health and Human Services, which has in its possession an enormous wealth of health care-related data from federally funded programs, has had and utilized data analytic capacities. The Department of Justice and various U.S. Attorneys’ Offices have also utilized data analytics in litigation. What is new is the Fraud Section’s systemic approach in building in-house data analytic capability for criminal prosecutions, and using it strategically to enhance national enforcements. The team is led by a Ph.D. in public health, with a strong background in research and data analysis. This level of capacity building and focus is nothing less than a game changer in white-collar criminal enforcement.
The use of data analytics is by no means restricted to health care. While in the Fraud Section, I worked with the Foreign Corrupt Practices Act (“FCPA”) Unit and the Federal Bureau of Investigation on developing data analytic methodologies for identifying potential FCPA violations through publicly available data. Similarly, I worked with prosecutors and analysts in the Securities and Financial and Fraud (“SFF”) Unit on ways to develop financial crimes profiles based on data analytics. Health Care, due to both the amount of data and level of expertise available, is perhaps the furthest along in utilizing the data analytics capacity by way of launching a formal team structure. The lack of a formal structure in the Fraud Section’s other two units (FCPA and SFF), however, does not mean data analytics is not being utilized with increasing appreciation and sophistication in investigations.
What does this mean for companies? Companies that focus on meaningful risk assessments and measuring the effectiveness of their compliance programs would have already been using data analytics in their programs. Companies whose compliance goal is merely to avoid enforcement by focusing on the minimum requirements of the U.S. Sentencing Guidelines, however, would be ignoring the use of data at their own peril. If you cannot decipher what data might be telling you about potential violations, the government might just find these violations before you do. The Fraud Section’s data analytics capacity building is a recognition of the importance of data science in compliance and investigations, and the move places it well ahead of most corporate compliance programs in the ability to detect crimes.
In my interactions with the corporate compliance community, I have found a common lack of appreciation and vast under-utilization of data. When I speak with representatives of compliance education programs in both academic and professional settings, my questions on how they teach the use of data is most often met with blank stares. I believe this data gap is an unfortunate disservice to the compliance profession, just as surely as it would have been to ignore the power of computer technology decades ago. The Fraud Section’s establishment of the Data Analytics Team is a recognition that data science is here to stay in the world of white-collar enforcement. The only question is whether the world of corporate compliance can catch up?