As expected, renters struggled to pay their April 1st rent. According to the report published by the National Multifamily Housing Council, approximately 31% of multifamily renters did not make their April 1st payment. This figure is up 12 percentage points from March 2020 and 13 percentage points from April 2019.

Dr. Albert Lee, Founding Partner of Summit Consulting, talks with Dr. Mark Hutson, Summit’s Manager of Data Science and Modeling & Statistics about his recent paper Predictive Analytics: the New Tool to Combat Fraud, Waste and Abuse.

As federal organizations continue to ramp up their use of quantitative analyses to inform decision-making, Summit is building tools and training our clients to help maximize the value of business functions. Summit specializes in making data readily accessible and easily-consumable with visualizations directly from a web browser. Our tools facilitate user-driven scenarios through a dynamic web interface, empowering staff and leaders to make decisions according to the most current data available. Tools like these augment, or even obviate, the need for cumbersome written reports and lengthy model documentation, providing more value for the end-user and greater flexibility in understanding what is truly going on within an organization.

Predictive analytics modeling is a statistical method that connects observable patterns to unobservable occurrences. For example, the IRS has access to tax filing data (observable patterns). Since rule-breakers usually attempt to hide their bad behavior, fraud can be difficult to detect. To address this problem, investigators can analyze data using special modeling techniques that detect filing patterns most closely associated with fraudulent behavior (unobservable occurrences). In this way, predictive analytics can help agencies identify these harmful patterns using already-available data.

Federal agencies collect vast amounts of data as part of their administrative duties. ("What is Administrative Data?") The U.S. Department of Labor requires retirement plans of a certain size to file an annual report (Form 5500) describing changes in assets and number of participants. The U.S. Security and Exchange Commission (SEC) examines publicly traded companies using Form 10-K, which summarizes companies’ financial performance. The U.S. Internal Revenue Service (IRS) is well known for the myriad of forms it uses to collect details about filing subjects.

Many Federal agencies in the United States enforce civil codes. Their responsibilities span from protecting retirement plans to monitoring stock trading to collecting taxes. For example, the U.S. Department of Labor, Employee Benefits Security Administration (EBSA) enforces the Employee Retirement Income Security Act of 1974 (ERISA), which requires EBSA to monitor retirement plans. The U.S. Security and Exchange Commission (SEC) enforces the Security Act of 1933, which requires SEC to monitor publicly traded companies. And everyone knows what the U.S. Internal Revenue Service (IRS) does. To save time and money, these agencies can use predictive analytics to find likely rule breakers. 

• Which areas of enforcement can predictive analytics support?  

• What kinds of tools are needed to apply the results of predictive analytics to civil enforcement?   

• And what should agencies keep in mind when applying predictive analytics to their enforcement efforts? 

*This is the fifth installment of our new blog series: Predictive Analytics in Enforcement. See our previous posts: What is Predictive Analytics?, Searching for Regulatory Violations, Technical Challenges with Non-Random Investigative Data, and Population Uncertainty and Lack of Information.*

Agencies charged with enforcing laws or regulations use many different methods to determine who or what to investigate. Good investigators will always rely on tips, hunches, and suspicious activities. But over the past decade, agencies have started using predictive models and other types of data analytics to determine what types of people, businesses, or other entities might be most likely to engage in all types of wrongdoing, including lawbreaking, noncompliance, or fraud.

In previous weeks, we discussed the technical challenges of using data from an investigative or enforcement process to model some types of compliance in an overall population. In this post, we explore why organizations use random sampling to gain a better overall picture of activity in the population.

*This is the fourth installment of our new blog series: Predictive Analytics in Enforcement. See our previous posts: What is Predictive Analytics?, Searching for Regulatory Violations, and Technical Challenges with Non-Random Investigative Data*

Last week, we discussed the technical challenges with using non-random investigative data for enforcement. In this post, we will explore the challenges of population uncertainty and information gaps.

Challenge: Uncertainty about the population under regulatory enforcement

Many agencies only periodically identify and collect information on all organizations under their regulatory purview; or, they only do this for the specific organizations which they investigate. For instance, the agency may know that all businesses with employees fall under their regulatory enforcement, but may not know exactly how many businesses this includes, or the specific identity of these businesses. For these agencies, a predictive model that focuses only on estimating an organization's probability of violation will be of limited utility, because the model’s results cannot be used to target for investigation organizations that are not identified in the agency’s data.  

Resolution: Leverage the explanatory power of the model’s predictors

*This is the third installment of our new blog series: Predictive Analytics in Enforcement. See our previous posts: What is Predictive Analytics? and Searching for Regulatory Violations*

Government agencies often use predictive analytics to improve the effectiveness and efficiency of regulatory enforcement. Their goal: minimize the time and cost required to find violators and change their behavior.

Administrative data on a particular agency’s investigation and enforcement activities is often the primary–and best–data source for predictive modeling. However, using an agency’s own administrative data can present unique challenges to developing an accurate and useful predictive model. Through our previous and current work in predictive analytics, Summit has developed several effective strategies for resolving these challenges.