Identity theft: a pernicious and costly fraud

On October 3, 2003, the Payment Cards Center of the Federal Reserve Bank of Philadelphia sponsored a workshop on identity theft to examine its growing impact on participants in our payments system. Avivah Litan, vice president and research director of financial services for Gartner Inc., led the workshop. The discussion began and this paper follows with a broad study of identity theft, at times compared with traditional payment fraud, and continues with an evaluation of its overall risk to consumers, merchants, and credit providers. The paper compares the incentives each such party has to address identity theft in concert with current market response to the crime. Finally, the paper concludes by posing several questions for further study. This paper supplements material from Litan’s presentation with additional research on the crime of identity theft.Fraud ; Identity theft

An Examination of User Detection of Business Email Compromise Amongst Corporate Professionals

With the evolution in technology and increase in utilization of the public Internet, Internet-based mobile applications, and social media, security risks for organizations have greatly increased. While corporations leverage social media as an effective tool for customer advertisements, the abundance of information available via public channels along with the growth in Internet connections to corporate networks including mobile applications, have made cyberattacks attractive for cybercriminals. Cybercrime against organizations is a daily threat and targeting companies of all sizes. Cyberattacks are continually evolving and becoming more complex that make it difficult to protect against with traditional security methods. Cybercriminals utilize email attacks as their most common method to compromise corporations for financial gain. Email attacks on corporations have evolved into very sophisticated scams that specifically target businesses that conduct wire transfers or financial transactions as part of their standard mode of operations. This new evolution of email driven attacks is called Business Email Compromise (BEC) attacks and utilize advanced social engineering, phishing techniques, and email hacking to manipulate employees into conducting fraudulent wire transfers that are intended for actual suppliers and business partners. One of the most common types of BEC attacks is the Chief Executive Officer (CEO) fraud, which are highly customized and targeted attacks aimed to impersonate corporate users that have authority to approve financial transactions and wire transfers in order to influence an employee to unknowingly conduct a fraudulent financial wire transfer. Thus, the main goal of this research study was to assess if there are any significant differences of corporate users’ detection skills of BEC attacks in a simulated test environment based on their personality attributes, using the Myers-Briggs Type Indicator® (MBTI®)’ 16 personalities® framework. BEC attacks have attributed to over $26 billion in corporate financial losses across the globe and are continually increasing. The human aspect in the cybersecurity has been a known challenge and is especially significant in direct interaction with BEC attacks. Furthermore, this research study analyzed corporate users’ attention span levels and demographics to assess if there are any significant differences on corporate users’ BEC attack detection skills. Moreover, this research study analyzed if there are any significant differences for BEC detection skills before and after a BEC awareness training. This research study was conducted by first developing an experiment to measure BEC detection and ensure validity via cybersecurity subject matter experts using the Delphi process. The experiment also collected qualitative and quantitative data for the participants’ performance measures using an application developed for the study. This research was conducted on a group of 45 corporate users in an experimental setting utilizing online surveys and a BEC detection mobile test application. This research validated and developed a BEC detection measure as well as the BEC awareness training module that were utilized in the research experiment. The results of the experiments were analyzed using analysis of variance (ANOVA) and analysis of covariance (ANCOVA) to address the research questions. It was found that there were that no statistically significant mean differences for Business Email Compromise Detection (BECD) skills between personality attributes of corporate professional participants, However, results indicated that there was a significant mean difference for BECD skills and span attention with a p\u3c.0001. Furthermore, there was a significant mean difference for BECD skills and span attention when controlled for gender with a p\u3c0.05. Furthermore, the results indicated that the BEC detection awareness training significantly improved the participant BEC detection skill with a p\u3c.0001. Moreover, following the training, it was found that female BEC detection test scores improved by 45% where the men BECD score improved by 31%. Recommendations for research and industry stakeholders are provided, including to corporations on methods to mitigate BEC attacks

Credit Card Security System and Fraud Detection Algorithm

Credit card fraud is one of the most critical threats affecting individuals and companies worldwide, particularly with the growing number of financial transactions involving credit cards every day. The most common threats are likely to come from database breaches and identity theft. All these threats threat put the security of financial transactions at severe risk and require a fundamental solution. This dissertation aims to suggest a secure online payment system that significantly improves credit card security. Our system can be particularly resilient to potential cyber-attacks, unauthorized users, man-in-the-middle, and guessing attacks for credit card number generation or illegal financial activities by utilizing a secure communication channel between the cardholder and server. Our system uses a shared secret and a verification token that allow both sides to communicate through encrypted information. Furthermore, our system is designed to generate a one-time credit card number at the user’s machine that is verified by the server without sharing the credit card number over the network. Our approach combines the machine learning (ML) algorithms with unique temporary credit card numbers in one integrated system, which is the first approach in the online credit card protection system. The new security system generates a one-time-use credit card number for each transaction with a predetermined amount of money. Simultaneously, the system can detect potential fraud utilizing ML algorithm with new critical features such as the IMEI or I.P. address, the transaction’s location, and other features. The contribution of this research is two-fold: (1) a method is proposed to generate a unique, authenticatable one-time credit card number to effectively defend against the database breaches, and (2) a credit card fraud prevention system is proposed with multiple security layers that are achieved by the integration of authentication, ML-based fraud detection, and the one-time credit card number generation. The dissertation improves consumers’ trust and confidence in the credit card system’s security and enhances satisfaction with credit cards’ various financial transactions. Further, the system uses the current online credit card infrastructure; hence it can be implemented without tangible infrastructure cost

Artificial Intelligence in Banking Industry: A Review on Fraud Detection, Credit Management, and Document Processing

AI is likely to alter the banking industry during the next several years. It is progressively being utilized by banks for analyzing and executing credit applications and examining vast volumes of data. This helps to avoid fraud and enables resource-heavy, repetitive procedures and client operations to be automated without any sacrifice in quality. This study reviews how the three most promising AI applications can make the banking sector robust and efficient. Specifically, we review AI fraud detection and prevention, AI credit management, and intelligent document processing. Since the majority of transactions have become digital, there is a great need for enhanced fraud detection algorithms and fraud prevention systems in banking. We argued that the conventional strategy for identifying bank fraud may be inadequate to combat complex fraudulent activity. Instead, artificial intelligence algorithms might be very useful.  Credit management is time-consuming and expensive in terms of resources. Furthermore, because of the number of phases involved, these processes need a significant amount of work involving many laborious tasks. Banks can assess new clients for credit services, calculate loan amounts and pricing, and decrease the risk of fraud by using strong AA/ML models to assess these large and varied data sets in real-time. Documents perform critical functions in the financial system and have a substantial influence on day-to-day operations. Currently, a large percentage of this data is preserved in email messages, online forms, PDFs, scanned images, and other digital formats. Using such a massive dataset is a difficult undertaking for any bank. We discuss how the artificial intelligence techniques that automatically pull critical data from all documents received by the bank, regardless of format, and feed it to the bank's existing portals/systems while maintaining consistency

The disease of corruption: views on how to fight corruption to advance 21st century global health goals

Corruption has been described as a disease. When corruption infiltrates global health, it can be particularly devastating, threatening hard gained improvements in human and economic development, international security, and population health. Yet, the multifaceted and complex nature of global health corruption makes it extremely difficult to tackle, despite its enormous costs, which have been estimated in the billions of dollars. In this forum article, we asked anti-corruption experts to identify key priority areas that urgently need global attention in order to advance the fight against global health corruption. The views shared by this multidisciplinary group of contributors reveal several fundamental challenges and allow us to explore potential solutions to address the unique risks posed by health-related corruption. Collectively, these perspectives also provide a roadmap that can be used in support of global health anti-corruption efforts in the post-2015 development agenda

Predicting fraud in mobile money transfer using case-based reasoning

This paper proposes an improved CBR approach for the identification of money transfer fraud in Mobile Money Transfer (MMT) environments. Standard CBR capability is augmented by machine learning techniques to assign parameter weights in the sample dataset and automate k-value random selection in k-NN classification to improve CBR performance. The CBR system observes users’ transaction behaviour within the MMT service and tries to detect abnormal patterns in the transaction flows. To capture user behaviour effectively, the CBR system classifies the log information into five contexts and then combines them into a single dimension, instead of using the conventional approach where the transaction amount, time dimensions or features dimension are used individually. The applicability of the proposed augmented CBR system is evaluated using simulation data. From the results, both dimensions show good performance with the context of information weighted CBR system outperforming the individual features approach

Evaluating the impact of AI on insurance: The four emerging AI- and data-driven business models [version 1; peer review: awaiting peer review]

The increasing capabilities of artificial intelligence (AI) are changing the way organizations operate and interact with users both internally and externally. The insurance sector is currently using AI in several ways but its potential to disrupt insurance is not clear. This research evaluated the implementation of AI-led automation in 20 insurance companies. The findings indicate four business models (BM) emerging: In the first model the insurer takes a smaller part of the value chain allowing others with superior AI and data to take a larger part. In the second model the insurer keeps the same model and value chain but uses AI to improve effectiveness. In the third model the insurer adapts their model to fully utilize AI and seek new sources of data and customers. Lastly in the fourth model a technology focused company uses their existing AI prowess, superior data and extensive customer base, and adds insurance provision

Data-Driven Implementation To Filter Fraudulent Medicaid Applications

There has been much work to improve IT systems for managing and maintaining health records. The U.S government is trying to integrate different types of health care data for providers and patients. Health care fraud detection research has focused on claims by providers, physicians, hospitals, and other medical service providers to detect fraudulent billing, abuse, and waste. Data-mining techniques have been used to detect patterns in health care fraud and reduce the amount of waste and abuse in the health care system. However, less attention has been paid to implementing a system to detect fraudulent applications, specifically for Medicaid. In this study, a data-driven system using layered architecture to filter fraudulent applications for Medicaid was proposed. The Medicaid Eligibility Application System utilizes a set of public and private databases that contain individual asset records. These asset records are used to determine the Medicaid eligibility of applicants using a scoring model integrated with a threshold algorithm. The findings indicated that by using the proposed data-driven approach, the state Medicaid agency could filter fraudulent Medicaid applications and save over $4 million in Medicaid expenditures