Enemy in the Mirror: How to Detect Financial Frauds

Have you ever wondered about the role of fraudsters in your daily life? We are a huge global community of people, and everything that happens above our heads affects our lives even when we do not really realize it. Cloned credit cards, money laundering, financial fraud are all well-known concepts, which are not thoroughly investigated.

In the era of Big Data, manual techniques of investigation, which for years have tried to find the frauds of all kinds, have lost any chance of success. The reason is the high mobility of money transfer in the modern financial world. The enormous innovations brought by the Internet have made ​​the lives of fraudsters even easier because you can lose track of them much more easily among the large volume of data and transactions at the international level.

There were born a number of contemporary practices in financial fraud detection using intelligent approaches, both statistical and computational. Research conducted by Jarrod West, Maumita Bhattacharya, and Rafiqul Islam from the School of Computing & Mathematics, Charles Sturt University, Australia, seeks to discover how can systems preventing financial frauds can be improved.

In general, the approaches based on computational intelligence are more effective than those based on statistical approach. The three academic remarked: “One statistical method seems to contradict this theory however: Bayesian belief networks were reported to be blackberries accurate than neural networks and decision trees.”

A golden mean is hybrid methods which use a mix of theories to structure new models that are based on the strengths of the various techniques. Another very interesting point of the study is the analysis of fraud types.

With regard to corporate frauds, the studies in this area, as those on money laundering and mortgage fraud are not so often within the range of studies done.

You can read the full paper here.