A Study on Various Applications of Data Mining and Supervised Learning Techniques in Business Fraud Detection

1.1 Introduction on Business Fraud

Corporate fraud indicates to happenings commenced by a personal or company that are executed in an untruthful or unlawful way and are calculated to give a benefit to the perpetrating individual or company. Corporate fraud schemes go beyond the scope of an employee's stated position and are marked by their complexity and economic impact on the business, other staffs, and outside parties.

Some examples of business frauds that have happened in the world have been: Enron, the energy company that tried to hide its true level of indebtedness through complex transactions with its subsidiary companies; WorldCom, a telecommunications firm that recorded expenditures of close to 4,000 million dollars as delayed overheads, in order to hide its actual losses; Global Crossing, a fiber optic company that recorded revenue as revenue that should be deferred; Vivendi, European communications entity that manipulated its Ebitda in the consolidation of its data with the business group, in order to acquire superior debt capacity; and Adelphia Communications, cable television society that was undercapitalized by granting enormous individual loans to its members with the agreement of its directors. These, among many others, are some of the most tarnished cases in the world of business fraud.

1.2 Introduction on Data Mining

Data Mining is a set of technique that applies to huge and composite databases. This is to eradicate the arbitrariness and determine the unseen outline. These data mining techniques are almost always computationally rigorous. We usage data mining tools, practices, and concepts for revealing designs in information. There are too many driving militaries existing. And, this is the cause why data mining has developed such a vital zone of education.

As data mining is having spacious applications. Thus, it is the young and promising field for the present generation. It has attracted a great deal of attention in the information industry and in society. Due to the wide availability of huge amounts of data and the imminent need for turning such data into useful information and knowledge. Thus, we use information and knowledge for applications ranging from market study. This is the cause why data mining, recognized as knowledge detection from information.

1.3 Introduction on Machine Learning Using Supervised Approach

Machine Learning is indisputably one of the most powerful and influential skills in today’s world. More significantly, we are distant from watching its complete possibilities. There is no uncertainty that it will continue to be making headlines for the foreseeable future. This article is designed as an introduction to the Machine Learning concepts, covering all the fundamental ideas without being too high level.

Machine learning is a tool for turning information into knowledge. In the past 50 years, there has been an explosion of data. This mass of data is useless unless we analyse it and find the patterns hidden within. Machine learning techniques are used to automatically find the valuable underlying patterns within complex data that we would otherwise struggle to discover. The hidden patterns and knowledge about a problem can be used to predict future events and perform all kinds of complex decision making.

Two of the most widely adopted machine learning methods are supervised learning which trains algorithms based on example input and output data that is labelled by humans, and unsupervised learning which provides the algorithm with no labelled data in order to allow it to find structure within its input data.