Using Blockchain Technology to Facilitate Anti-Money Laundering Efforts

Money laundering can be defined as any act or attempted act to conceal or disguise the identity of illegally obtained proceeds so that they appear to have originated from legitimate sources (Money Laundering, 2016). It is difficult to determine the magnitude of money laundering because these illicit financial flows remain hidden (Schott, 2006). A report issued by the United Nations Office on Drugs and Crime (UNODC) quoted that the total of all criminal proceeds amounted to $2.1 trillion in 2009. The study also shows that “Less than 1 percent of global illicit financial flows are currently seized and frozen” (Pietschmann & Walker, 2012). This is concerning because money laundering not only enables the operation of criminal organizations such as drug and human traffickers but can also significantly distort the economies in which they enter. The Financial Action Task Force (FATF) is an inter-governmental policy-making body that has helped to promote anti-money laundering efforts since its formation in 1989. It has issued 40 recommendations to fight money laundering and nine special recommendations to combat terrorist financing which have been adopted by 32 countries (About - Financial Action Task Force, 2016). Unfortunately, implementing these strategies has proved to be difficult for both developed and lesser developed countries. According to a study conducted by PricewaterhouseCoopers in 2016, “over the last few years, in the U.S. alone, nearly a dozen global financial institutions have been assessed fines in the hundreds of millions to billions of dollars for money laundering and/or sanctions violations (PricewaterhouseCoopers, 2016). It stands to say that if financial institutions are having difficulties implementing frameworks to prevent and detect money laundering, then our enforcement agencies are unable to adequately address the issue as well. A new hurdle that enforcement agencies have had to face is the emergence of Bitcoin, as well as other cryptocurrencies, that can be described as “a digital currency and online payment system in which encryption techniques are used to regulate the generation of units of currency and verify the transfer of funds, operating independently of a central bank” (Swan, 2015). Being an often unrecognized currency, many banks and financial institutions have not had to worry about modifying their compliance programs. The biggest benefit of cryptocurrencies to money launderers is its decentralized nature. There is no governing authority, as members of the network handle issuances and payments. Once a disruptive technology, Bitcoin is beginning to lose momentum for a number of reasons and some its strongest proponents are now referring to it as nothing more than an experiment. The purpose of this paper is not to examine Bitcoin, but rather its underlying technology that has been found to be the actual value: blockchain. After providing a brief overview of the technology and the hurdles that financial institutions face when implementing anti-money laundering compliance programs, the possible ways in which blockchain can help alleviate these difficulties will be examined

Detecting Anterior Cruciate Ligament Tears and Posterolateral Corner Injuries on Magnetic Resonance Imaging

Introduction: Anterior Cruciate Ligament (ACL) tears are an extremely common orthopedic injury, with an incidence ranging from 39-52 per 100,000. Knee Magnetic Resonance Imaging (MRI) scans are the gold standard for diagnosing ACL tears and their comorbidities, such as posterolateral corner injuries; the results of these scans determine the appropriate treatment needed for patients. There is evidence that machine learning can be used to automate the detection of pathology on MRI, and we hypothesize that we can train a neural network machine learning model to accurately interpret ACL injuries and posterolateral corner injuries. Methods: We will be analyzing over 1000 knee MRIs including those that are normal, those with ACL tears, and those with ACL tears with posterolateral corner injuries. First, we will manually annotate the knee MRIs to classify them appropriately. We will then train a convoluted neural network machine learning model on ~80% of the data, and use the remaining ~20% to test its accuracy. We will compare the accuracy of our model to the accuracy of musculoskeletal radiologists. Results: We anticipate that our model will have comparable accuracy predicting ACL tears and posterolateral corner injuries to that of musculoskeletal radiologists. By having access to our model’s predictions, we expect radiologists will be able to detect ACL tears with posterolateral corner injuries with improved accuracy and speed. Discussion: While we do not have results yet, we anticipate that our model will be an early step to developing useful tools that aid radiologists. Our model will be trained on a large dataset which will increase its generalizability for future implementation. Radiologists can use our model’s predictions to aid them in diagnosis of pathology on knee MRI. We expect that improved diagnosis will improve patient treatment outcomes

Readmission Risk Assessment Tool for Stroke Patients

Introduction: Strokes are one of the leading causes of morbidity and mortality in the world and its cost of management has vastly increased; an effective prediction tool that utilizes artificial intelligence to lower the rate of stroke-related readmissions has the potential to lower healthcare costs and increase the quality of provider care. We hypothesize that machine learning techniques are superior to traditional statistics when determining the likelihood of 30-day readmission for Jefferson’s stroke patients. Methods: Jefferson’s existing data on stroke patients were cleaned, aggregated, and prepared to be split into train and test sets. Using the train sets, machine learning (ML) models such as Random Forest, Support Vector Machines, and Neural Networks were trained to assess the risk of readmission. Each model’s accuracy and precision were captured in the form of confusion matrices, AUCs, and more to reveal the most superior ML method in assessing this risk. These results were then compared to the readmission risk determined by traditional statistics. Results: After training the ML models, the test sets were inputted to determine how accurately they could predict a stroke patient’s chance of readmission with new data. Traditional statistics (in the form of logistic regression) showed an accuracy of 84%. The ML methods utilized resulted in the following accuracies: Random Forest at 95.50%, SVM at 94.79%, and Neural Networks at 95.40%. Discussion: This study not only demonstrates that machine learning methods are superior to traditional statistics in regard to determining the 30-day readmission risks for Jefferson stroke patients, but it also shows that the Random Forest model is the most accurate in doing so. The potential implications of this tool are large; its use can be seen at both the patient and the hospital levels by improving costs for the patient and the hospital as well as improving stroke education and care