Occupational Fraud Detection Through Visualization

Occupational fraud affects many companies worldwide causing them economic loss and liability issues towards their customers and other involved entities. Detecting internal fraud in a company requires significant effort and, unfortunately cannot be entirely prevented. The internal auditors have to process a huge amount of data produced by diverse systems, which are in most cases in textual form, with little automated support. In this paper, we exploit the advantages of information visualization and present a system that aims to detect occupational fraud in systems which involve a pair of entities (e.g., an employee and a client) and periodic activity. The main visualization is based on a spiral system on which the events are drawn appropriately according to their time-stamp. Suspicious events are considered those which appear along the same radius or on close radii of the spiral. Before producing the visualization, the system ranks both involved entities according to the specifications of the internal auditor and generates a video file of the activity such that events with strong evidence of fraud appear first in the video. The system is also equipped with several different visualizations and mechanisms in order to meet the requirements of an internal fraud detection system

A Framework for Occupational Fraud Detection by Social Network Analysis

International audienceThis paper explores issues related to occupational fraud detection. We observe over the past years, a broad use of network research across social and physical sciences including but not limited to social sharing and filtering, recommendation systems, marketing and customer intelligence, counter intelligence and law enforcement. However, the rate of social network analysis adoption in organizations by control professionals or even by academics for insider fraud detection purpose is still very low. This paper introduces the OFD – Occupational Fraud Detection framework, based on formal social network analysis and semantic reasoning principles by taking a design science research perspective

Automating Vendor Fraud Detection in Enterprise Systems

Fraud is a multi-billion dollar industry that continues to grow annually. Many organizations are poorly prepared to prevent and detect fraud. Fraud detection strategies are intended to quickly and efficiently identify fraudulent activities that circumvent preventative measures. In this paper, we adopt a DesignScience methodological framework to develop a model for detection of vendor fraud based on analysis of patterns or signatures identified in enterprise system audit trails. The concept is demonstrated by developing prototype software. Verification of the prototype is achieved by performing a series of experiments. Validation is achieved by independent reviews from auditing practitioners. Key findings of this study are: (a) automating routine data analytics improves auditor productivity and reduces time taken to identify potential fraud; and (b) visualizations assist in promptly identifying potentially fraudulent user activities. The study makes the following contributions: (a) a model for proactive fraud detection; (b) methods for visualizing user activities in transaction data; and (c) a stand-alone Monitoring and Control Layer (MCL) based prototype

Automating Vendor Fraud Detection in Enterprise Systems

Fraud is a multi-billion dollar industry that continues to grow annually. Many organisations are poorly prepared to prevent and detect fraud. Fraud detection strategies are intended to quickly and efficiently identify fraudulent activities that circumvent preventative measures. In this paper we adopt a Design-Science methodological framework to develop a model for detection of vendor fraud based on analysis of patterns or signatures identified in enterprise system audit trails. The concept is demonstrated be developing prototype software. Verification of the prototype is achieved by performing a series of experiments. Validation is achieved by independent reviews from auditing practitioners. Key findings of this study are: i) automating routine data analytics improves auditor productivity and reduces time taken to identify potential fraud, and ii) visualisations assist in promptly identifying potentially fraudulent user activities. The study makes the following contributions: i) a model for proactive fraud detection, ii) methods for visualising user activities in transaction data, iii) a stand-alone MCL-based prototype.</p

Analyzing Proactive Fraud Detection Software Tools and the Push for Quicker Solutions

This paper focuses on proactive fraud detection software tools and how these tools can help detect and prevent possible fraudulent schemes. In addition to relying on routine audits, companies are designing proactive methods that involve the inclusion of software tools to detect and deter instances of fraud and abuse. This paper discusses examples of companies using ACL and SAS software programs and how the software tools have positively changed their auditing systems. Novelis Inc., an aluminum and recycling company, implemented ACL into their internal audit software system. Competitive Health Analytics (Division of Humana) implemented SAS in order to improve their overall health analytics and databases. The use of these tools enabled them to monitor the safety of their products and detect red flags of healthcare fraud. Though fraud is rampant through our systems, this paper will investigate how these different software programs have helped detect fraud in schemes such as asset misappropriation, bribery and corruption, and financial statement fraud. This paper posits that companies can quickly uncover potential fraud schemes without exhausting a lot of significant amounts of time and money by implementing proactive fraud detection software tools. The paper will also address software tools used for managing the risk of fraud along with specific benefits incurred through use of these tools

INTERNAL CONTROL AND FRAUD PREVENTION: PRIOR RESEARCH ANALYSIS

The focus of this study is to analyze prior research on fraud detection and prevention. Most researchers agree that strong internal controls are an influencing factor on fair financial reporting and fraud prevention and detection. Financial statement and employee fraud can be very expensive to businesses and the economy as a whole. The establishment and evaluation of the internal control methods and procedures can decrease fraudulent events and losses. Accounting professionals, CPA’s, and tax preparers are the first to detect “red flags” in business activities and must work together with boards of directors, CFO’s, and small business owners. Simple methods, such as ratio analyses can help to signal early signs of fraudulent events and prevent future damages.&nbsp; Implementation of fraud prevention measures are the most efficient deterrent. Some of the most effective controls like, job rotation, mandatory vacations, training, fraud hotlines, and surprise audits, need not be expensive and should be employed by all businesses. Unfortunately, the most important and effective fraud prevention techniques are seldom applied by businesses. Surprisingly, the least effective and most expensive measures, like external audits, are more frequently employed. As reported in this review of the literature, most businesses focus on fraud detection, while fraud prevention and implementing proper internal controls would result in better prevention of financial losses. DOI: https://doi.org/10.15544/ssaf.2014.1

Assessing the Usefulness of Visualization Tools to Investigate Hidden Patterns with Insider Attack Cases

The insider threat is a major concern for organizations. Open markets, technological advances, and the evolving definition of employee have exacerbated the insider threat. Insider threat research efforts are focusing on both prevention and detection techniques. However, recent security violation trends highlight the damage insider attacks cause organizations and illuminate why organizations and researchers must develop new approaches to this challenge. Although fruitful research is being conducted and new technologies are being applied to the insider threat problem, companies remain susceptible to the costly damage generated by insider threat actions. This research explored how visualization tools may be useful in highlighting patterns or relationships in insider attack case data and sought to determine if visualization software can assist in generating hypotheses for future insider threat research. The research analyzes cases of insider attack crimes committed during the period of 1998 to 2004 with an information visualization tool, IN-SPIRE. The results provide some evidence that visualization tools are useful in both finding patterns and generating hypotheses. By identifying new knowledge from insider threat cases, current insider threat models may be refined and other potential solutions may be discovered