Statistical Methods for Fighting Financial Crimes
Financial crimes affect millions of people every year and financial institutions must employ methods to protect themselves and their customers. The use of statistical methods to address these problems faces many challenges. Financial crimes are rare events that lead to extreme class imbalances. Criminals deliberately attempt to conceal the nature of their actions and quickly change their strategies over time, resulting in class overlap and concept drift. In some cases, legal constraints and investigation delays make it impossible to actually verify suspected crimes in a timely manner, resulting in class mislabeling or unknown labels. In addition, the volume and complexity of financial data require algorithms to be not only effective, but also efficiently trained and executed. This article focuses on two important types of financial crimes: fraud and money laundering. It discusses some of the traditional statistical techniques that have been applied as well as more recent machine learning and data mining algorithms. The goal of the article is to introduce the subject and to provide a survey of broad classes of methodologies accompanied by selected illustrative examples.[1]
Financial Crimes
This article begins by examining the evidence on the extent and costs of financial crimes. It includes a broad variety of deceptions against a spectrum of poor and wealthy individuals and businesses. It present an analysis of organization of financial crimes at a general level and then discusses subtypes of fraud, based on their techniques and victim sectors. It provides an understanding of the organization of fraud in terms of how would-be offenders confront problems of gaining finance, gaining access to crime opportunities, and retaining their freedom and crime proceeds. There is a discussion of alternative model of thinking about financial crimes and involves clustering them as the skill sets, contacts, start-up capital, and running costs that they require. It concludes with an examination of the major regulatory and criminal justice policy options. [2]
Financial crimes: the constant challenge of seeking effective prevention solutions
Purpose
Financial market‐related crimes seem to continually increase in number as well as in the amount of illicit profits. This emerging situation has obliged governments and self‐regulated bodies to act aggressively on the issue. This paper provides a snapshot of the evolution timeline of financial crimes and discussion in support of the fight against this plague.
Design/methodology/approach
Based on financial crime literature and field work.
Findings
Improvement in the expertise and degree of refinement employed by both organized crime and criminal businessmen.
Research limitations/implications
Some information originates from confidential sources and consequently could not be further developed.
Originality/value
Contemporary picture of the current situation. Some recommendations were submitted to regulatory authorities who are examining and adjusting their actions accordingly.[3]
Assessing the Relationship between Regulatory Framework and Forensic Accounting, the Nigerian Experience
The primary objective of this study is to investigate the relationship between regulatory framework and forensic accounting practice in Nigeria. The study adopted a survey research with questionnaire drawn to elicit responses. The results of this study show that Nigeria Evidence Act 2011 as amended and EFCC Act 2000 give much impetus and legal backing to the effective functioning of forensic accountants in Nigeria but Political will, enabling environment and modern infrastructures which are necessary in combating financial crime cannot be ignored.[4]
Organised Crime, Foreign Direct Investment and Economic Growth in Ghana
Aims: Ghana has put measures in place to increase its economic growth. It is established that a reduction in crime rate is a good impetus for growth. The study therefore investigated the relationship between organised crime, FDI and economic growth in Ghana.
Study Design: The study uses an Exploratory Design.
Place and Duration of the Study: Ghana, between the periods 2000 to 2014.
Methodology: This was done by means of the Autoregressive Distributed Lag (ARDL) approach.
Results: The study found a cointegrating relationship between economic growth and its determinants. The regression results show that organised crime and FDI are important determinants of economic growth in Ghana since they exerted statistically significant negative and positive effects on economic growth respectively both in the short-run and long-run in Ghana. In addition, interest rate, and inflation also exerted negative and statistically significant effects on economic growth both in the short-run and long-run in Ghana. Further, real effective exchange rate, physical capital and labour force exerted positive and statistically significant effects on economic growth both in the short-run and long-run in Ghana.
Conclusion: It is therefore recommended that policymakers should put in pragmatic measures to reduce crime rate, increase FDI inflows, physical capital, and labour force, as well as maintaining low inflationary rate. Finally, Bank of Ghana should maintain a stable exchange rate while financial institutions are to reduce their interest rate on lending.[5]
Reference
[1] Sudjianto, A., Nair, S., Yuan, M., Zhang, A., Kern, D. and Cela-Díaz, F., 2010. Statistical methods for fighting financial crimes. Technometrics, 52(1), pp.5-19.
[2] Levi, M., 2009. Financial crimes. In The Oxford handbook of crime and public policy.
[3] Michel, P., 2008. Financial crimes: the constant challenge of seeking effective prevention solutions. Journal of Financial Crime.
[4] Micah, L.C. and Ruth, T.O., 2014. Assessing the Relationship between Regulatory Framework and Forensic Accounting, the Nigerian Experience. Journal of Economics, Management and Trade, pp.2095-2106.
[5] Agyapong, D., Asiamah, M. and Addo-Danquah, M.Y.T., 2016. Organised Crime, Foreign Direct Investment and Economic Growth in Ghana. Journal of Economics, Management and Trade, pp.1-12.