A wide variety of businesses and government agencies support the U.S. real estate market. Examples would include sales agents, national lenders, local credit unions, private mortgage and title insurers, and government sponsored entities (Freddie Mac and Fannie Mae), to name a few. The financial performance and overall success of these organizations depends in large part on the health of the overall real estate market. According to the National Association of Home Builders (NAHB), the construction of one single-family home of average size creates the equivalent of nearly 3 new jobs for a year (Greiner, 2015). The economic impact is significant, with residential construction and related activities contributing approximately 5 percent to overall gross domestic product. With these data points in mind, the ability to accurately predict housing trends has become an increasingly important function for organizations engaged in the real estate market. The government bailouts of Freddie Mac and Fannie Mae in July 2008, following the severe housing market collapse which began earlier that year, serve as an example of the risks associated with the housing market. The housing market collapse had left the two firms, which at the time owned or guaranteed about $5 trillion of home loans, in a dangerous and uncertain financial state (Olick, 2018). Countrywide Home Loans, Indy Mac, and Washington Mutual Bank are a few examples of mortgage banks that did not survive the housing market collapse and subsequent recession. In the wake of the financial crisis, businesses within the real estate market have recognized that predicting the direction of real estate is an essential business requirement. A business acquisition by Radian Group, the Philadelphia-based mortgage insurance company, illustrates the importance of predictive modeling for the mortgage industry. In January 2019, Radian Group acquired Five Bridges Advisors, a Maryland-based firm which develops data analytics and econometric predictive models leveraging artificial intelligence and machine learning techniques (Blumenthal, 2019).

Date of Award

Summer 9-15-2019

Degree Type


First Advisor

Margaret McCoey


This capstone examines the developing issue of money laundering through online gambling sites which are extensions of casinos located within the United States. The online gambling scene is rapidly growing; and these venue will soon become targets for money laundering by criminals, human traffickers, and even terrorists. "Internet gambling and online capabilities have become a haven for money laundering activities...internet gambling operations are vulnerable to be used, not only for money laundering, but also criminal activities ranging from terrorist financing to tax evasion” (Fbi Confirms Online Gambling Opens Door To Fraud, Money Laundering; Age Verification Software Ineffective. (2009, Dec 04)

This paper will discuss how casinos which host online gambling must focus on protecting their transactions from money laundering. There must be internal controls to “red flag” any suspicious transactions as well as highly trained staff to review such activity. This paper will examine specific areas which online casinos web sites are susceptible to money laundering and offer solutions to identify these transactions.