Bachelor Thesis from the year 2017 in the subject Computer Science - Commercial Information Technology, grade: 1.3, Heilbronn University, language: English, abstract: White-collar crime is and has always been an urgent issue for the society. In recent years, white-collar crime has increased dramatically by technological advances. The studies show that companies are affected annually by corruption, balance-sheet manipulation, embezzlement, criminal insolvency and other economic crimes. The companies are usually unable to identify the damage caused by fraudulent activities. To prevent fraud, companies have the opportunity to use intelligent IT approaches. The data analyst or the investigator can use the data which is stored digitally in today’s world to detect fraud. In the age of Big Data, digital information is increasing enormously. Storage is cheap today and no longer a limited medium. The estimates assume that today up to 80 percent of all operational information is stored in the form of unstructured text documents. This bachelor thesis examines Data Mining and Text Mining as intelligent IT approaches for fraud detection in white-collar crime. Text Mining is related to Data Mining. For a differentiation, the source of the information and the structure is important. Text Mining is mainly concerned with weak- or unstructured data, while Data Mining often relies on structured sources. At the beginning of this bachelor thesis, an insight is first given on white-collar crime. For this purpose, the three essential tasks of a fraud management are discussed. Based on the fraud triangle of Cressey it is showed which conditions need to come together so that an offender commits a fraudulent act. Following, some well-known types of white-collar crime are considered in more detail. Text Mining approach was used to demonstrate how to extract potentially useful knowledge from unstructured text. For this purpose, two self-generated e-mails were converted into struc-tured format. Moreover, a case study will be conducted on fraud detection in credit card da-taset. The dataset contains legitimate and fraudulent transactions. Based on a literature research, Data Mining techniques are selected and then applied on the dataset by using various sampling techniques and hyperparameter optimization with the goal to identify correctly pre-dicted fraudulent transactions. The CRISP-DM reference model was used as a methodical procedure.

This book examines internal fraud investigations in public and private organizations. It provides a theoretical framework of white-collar crime and convenience theory, to examine a number of case studies, including some cases brought to light by the Panama Papers. Investigating white-collar crime is distinguished from other types of crime by: concealment of the crime rather than the criminal, victims who may be unaware of the crime and not directly visible to the criminal, and the resources available to suspects. It requires a unique strategy and a unique set of tools. This work provides insight into a number of internal investigation reports that are normally not publicly available. It will be of interest to researchers in criminology and criminal justice, particularly with an interest in white collar crime and corruption, as well as related fields such as business, management, economics, and public administration.

The indispensable guide to detecting and solving financial crime inthe office Low-level financial crimes are a fact of life in the modernworkplace. Individually these crimes are rarely significant enoughto warrant the hiring of professional investigators, but if leftunchecked, small crimes add up to big losses. In companies withoutdedicated fraud investigators, detecting and solving low-levelcrimes generally falls to managers and internal auditors. FinancialCrime Investigation and Control offers tips, tools, and techniquesto help professionals who lack investigative experience stem thetide of small financial crimes before it becomes a tsunami. Inside you'll find expert guidance on investigating and uncoveringcommon types of fraud, including: * Credit card fraud * Consumer fraud * Kickbacks * Bid rigging * Inflated invoices * Inventory theft * Theft of cash * Travel and subsistence claims * Check fraud * ID fraud * Ghost employees * Misappropriation schemes * Computer-related crime * Financial statement fraud

This open access book examines the magnitude, causes of, and reactions to white-collar crime, based on the theories and research of those who have uncovered various forms of white-collar crime. It argues that the offenders who are convicted represent only ‘the tip of the iceberg’ of a much greater problem: because white-collar crime is forced to compete with other kinds of financial crime like social security fraud for police resources and so receives less attention and fewer investigations. Gottschalk and Gunnesdal also offer insights into estimation techniques for the shadow economy, in an attempt to comprehend the size of the problem. Holding broad appeal for academics, practitioners in public administration, and government agencies, this innovative study serves as a timely starting point for examining the lack of investigation, detection, and conviction of powerful white-collar criminals.

Very few white-collar criminals are detected. They are able to commit and conceal their fraud to benefit their organization or themselves, and continue in their privileged professional positions as members of the elite in society. When rumors of misconduct and crime occur, white-collar offenders are often so powerful that nothing happens to them. Some are too powerful to detect, investigate, prosecute, and jail. Whistleblowers play an important role in detection. They detect crime signals and send messages to people who can do something about the situation. They may report internally to executives or auditors, or they may send messages externally to journalists or public authorities. After discussion of white-collar fraud in the perspective of convenience theory, this book moves into its core of fraud signal detection, detailing the key terms signal strength, signal alertness, pattern recognition, and personal experience. The book then presents four case studies where whistleblowers reported fraud suspicions. After whistleblowing, fraud examiners were hired to reconstruct past events in private internal investigations.