Electronic fraud detection in the U.S. Medicaid Healthcare Program: lessons learned from other industries

It is estimated that between $600 and$850 billion annually is lost to fraud, waste, and abuse in the US healthcare system,with $125 to$175 billion of this due to fraudulent activity (Kelley 2009). Medicaid, a state-run, federally-matchedgovernment program which accounts for roughly one-quarter of all healthcare expenses in the US, has been particularlysusceptible targets for fraud in recent years. With escalating overall healthcare costs, payers, especially government-runprograms, must seek savings throughout the system to maintain reasonable quality of care standards. As such, the need foreffective fraud detection and prevention is critical. Electronic fraud detection systems are widely used in the insurance,telecommunications, and financial sectors. What lessons can be learned from these efforts and applied to improve frauddetection in the Medicaid health care program? In this paper, we conduct a systematic literature study to analyze theapplicability of existing electronic fraud detection techniques in similar industries to the US Medicaid program

Factor Analysis-based Investigation into Financial Crime Related Issues in Nigeria

This paper proposes sixteen indices that were considered very important for the analysis of financial crime related issues in Nigeria. The indices were listed in the questionnaire that was administered on the FCT and thirty out of thirty-six states that span the six geo-political zones of Nigeria to obtain relevant data. Copies of the questionnaire were administered during meetings with stakeholders of banks, insurance companies, educational institutions and other relevant government and private owned establishments. The data obtained were subjected to factor analysis by principal component using SPSS. The analysis identified policies and regulations, responses and management, capacity building and awareness and litigation as the major issues to be addressed if financial crimes are to be checked. The percentage of the contributory effect of these issues and the degree of relevance of their associated indices were determined and found to be less than 100, indicating that the indices of some extraneous issues were not considered in the research instrument. Such issues include but not limited to economic status and cultural and societal impacts. Moreover, a coefficient score matrix was generated and used to estimate and rank the contribution of each respondent to the extracted issues

A comparative analysis of decision trees vis-a-vis other computational data mining techniques in automotive insurance fraud detection

The development and application of computational data mining techniques in financial fraud detection and business failure prediction has become a popular cross-disciplinary research area in recent times involving financial economists, forensic accountants and computational modellers. Some of the computational techniques popularly used in the context of - financial fraud detection and business failure prediction can also be effectively applied in the detection of fraudulent insurance claims and therefore, can be of immense practical value to the insurance industry. We provide a comparative analysis of prediction performance of a battery of data mining techniques using real-life automotive insurance fraud data. While the data we have used in our paper is US-based, the computational techniques we have tested can be adapted and generally applied to detect similar insurance frauds in other countries as well where an organized automotive insurance industry exists

Suspicious activity reporting using dynamic bayesian networks

AbstractSuspicious activity reporting has been a crucial part of anti-money laundering systems. Financial transactions are considered suspicious when they deviate from the regular behavior of their customers. Money launderers pay special attention to keep their transactions as normal as possible to disguise their illicit nature. This may deceive the classical deviation based statistical methods for finding anomalies. This study presents an approach, called SARDBN (Suspicious Activity Reporting using Dynamic Bayesian Network), that employs a combination of clustering and dynamic Bayesian network (DBN) to identify anomalies in sequence of transactions. SARDBN applies DBN to capture patterns in a customer’s monthly transactional sequences as well as to compute an anomaly index called AIRE (Anomaly Index using Rank and Entropy). AIRE measures the degree of anomaly in a transaction and is compared against a pre-defined threshold to mark the transaction as normal or suspicious. The presented approach is tested on a real dataset of more than 8 million banking transactions and has shown promising results

Machine learning methods to detect money laundering in the bitcoin blockchain in the presence of label scarcity

Lorenz, J., Silva, M. I., Aparício, D., Ascensão, J. T., & Bizarro, P. (2020). Machine learning methods to detect money laundering in the bitcoin blockchain in the presence of label scarcity. In ICAIF 2020 - 1st ACM International Conference on AI in Finance (pp. 1-8). [3422549] (ICAIF 2020 - 1st ACM International Conference on AI in Finance). Association for Computing Machinery, Inc. https://doi.org/10.1145/3383455.3422549Every year, criminals launder billions of dollars acquired from serious felonies (e.g., terrorism, drug smuggling, or human trafficking), harming countless people and economies. Cryptocurrencies, in particular, have developed as a haven for money laundering activity. Machine Learning can be used to detect these illicit patterns. However, labels are so scarce that traditional supervised algorithms are inapplicable. Here, we address money laundering detection assuming minimal access to labels. First, we show that existing state-of-the-art solutions using unsupervised anomaly detection methods are inadequate to detect the illicit patterns in a real Bitcoin transaction dataset. Then, we show that our proposed active learning solution is capable of matching the performance of a fully supervised baseline by using just 5% of the labels. This solution mimics a typical real-life situation in which a limited number of labels can be acquired through manual annotation by experts.publishersversionpublishe

Factor Analysis-based Investigation into Financial Crime Related Issues in Nigeria

This paper proposes sixteen indices that were considered very important for the analysis of financial crime related issues in Nigeria. The indices were listed in the questionnaire that was administered on the FCT and thirty out of thirty-six states that span the six geo-political zones of Nigeria to obtain relevant data. Copies of the questionnaire were administered during meetings with stakeholders of banks, insurance companies, educational institutions and other relevant government and private owned establishments. The data obtained were subjected to factor analysis by principal component using SPSS. The analysis identified policies and regulations, responses and management, capacity building and awareness and litigation as the major issues to be addressed if financial crimes are to be checked. The percentage of the contributory effect of these issues and the degree of relevance of their associated indices were determined and found to be less than 100, indicating that the indices of some extraneous issues were not considered in the research instrument. Such issues include but not limited to economic status and cultural and societal impacts. Moreover, a coefficient score matrix was generated and used to estimate and rank the contribution of each respondent to the extracted issues

The use of predictive analytics in finance

Statistical and computational methods are being increasingly integrated into Decision Support Systems to aid management and help with strategic decisions. Researchers need to fully understand the use of such techniques in order to make predictions when using financial data. This paper therefore presents a method based literature review focused on the predictive analytics domain. The study comprehensively covers classification, regression, clustering, association and time series models. It expands existing explanatory statistical modelling into the realm of computational modelling. The methods explored enable the prediction of the future through the analysis of financial time series and cross-sectional data that is collected, stored and processed in Information Systems. The output of such models allow financial managers and risk oversight professionals to achieve better outcomes. This review brings the various predictive analytic methods in finance together under one domain

The Possibility of Predictions in Auditor’s Opinion: The Case of the Serbian Tobacco Industry

Research Questions: This paper investigated whether there is a possibility for predictions of an auditor’s opinion that can be used to predict, in an extremely accurate way, future developments in one company. Motivation: The research of Dopuch, Holhausen and Leftwich (1987); Kirkos, Spathis and Manolopoulos (2007) or Kirskos (2012) and Kim and Upneja (2014) open space for new challenges for using auditing methods.The most trying task is to find a technique that will be able to timely, accurately and with the least waste of resources respond to the challenge. The fact that auditors are forced to expand the scope and purpose of the audit work, respecting new risks that are continually changing represents the primary inspiration for this paper. Idea: Our goal was to explore whether one of the possible techniques for prediction the auditor’s opinion – multivariate discriminant analysis – can precisely predict a correct future audit opinion and whether this analysis is useful for finding solutions to performing predictions. Data: The analysis was conducted using data from financial statements of 4 Serbian tobacco companies of years 2011, 2012, 2013, 2014 and 2015 published by the Serbian Business Registers Agency. Tools: The presented research, based on theoretical and mathematical support, uses statistical software tools Statistica. Findings: The application of discriminant analysis in Serbian tobacco companies showed statistically major variables of the balance sheet, manely “Intangible assets", "Supplies" and "Liabilities". Following these variables, we obtained results which we used as the predictors. The outcome of our preliminary investigation presented accurate and correct prediction which is also confirmed by historical data. The result of this investigation can be used for further more complex investigations when using some variables that will lead to discriminatory analysis for more classification groups to mark and rank the most significant variables for expressing the audit opinion. Contribution: Provided information is important for every business, because every entity that is listed on the business market aims to be as better as possible, and find out and exploit the possibility of avoiding a negative result

An academic review: applications of data mining techniques in finance industry

With the development of Internet techniques, data volumes are doubling every two years, faster than predicted by Moore’s Law. Big Data Analytics becomes particularly important for enterprise business. Modern computational technologies will provide effective tools to help understand hugely accumulated data and leverage this information to get insights into the finance industry. In order to get actionable insights into the business, data has become most valuable asset of financial organisations, as there are no physical products in finance industry to manufacture. This is where data mining techniques come to their rescue by allowing access to the right information at the right time. These techniques are used by the finance industry in various areas such as fraud detection, intelligent forecasting, credit rating, loan management, customer profiling, money laundering, marketing and prediction of price movements to name a few. This work aims to survey the research on data mining techniques applied to the finance industry from 2010 to 2015.The review finds that Stock prediction and Credit rating have received most attention of researchers, compared to Loan prediction, Money Laundering and Time Series prediction. Due to the dynamics, uncertainty and variety of data, nonlinear mapping techniques have been deeply studied than linear techniques. Also it has been proved that hybrid methods are more accurate in prediction, closely followed by Neural Network technique. This survey could provide a clue of applications of data mining techniques for finance industry, and a summary of methodologies for researchers in this area. Especially, it could provide a good vision of Data Mining Techniques in computational finance for beginners who want to work in the field of computational finance