FraudDroid: Automated Ad Fraud Detection for Android Apps

Although mobile ad frauds have been widespread, state-of-the-art approaches in the literature have mainly focused on detecting the so-called static placement frauds, where only a single UI state is involved and can be identified based on static information such as the size or location of ad views. Other types of fraud exist that involve multiple UI states and are performed dynamically while users interact with the app. Such dynamic interaction frauds, although now widely spread in apps, have not yet been explored nor addressed in the literature. In this work, we investigate a wide range of mobile ad frauds to provide a comprehensive taxonomy to the research community. We then propose, FraudDroid, a novel hybrid approach to detect ad frauds in mobile Android apps. FraudDroid analyses apps dynamically to build UI state transition graphs and collects their associated runtime network traffics, which are then leveraged to check against a set of heuristic-based rules for identifying ad fraudulent behaviours. We show empirically that FraudDroid detects ad frauds with a high precision (93%) and recall (92%). Experimental results further show that FraudDroid is capable of detecting ad frauds across the spectrum of fraud types. By analysing 12,000 ad-supported Android apps, FraudDroid identified 335 cases of fraud associated with 20 ad networks that are further confirmed to be true positive results and are shared with our fellow researchers to promote advanced ad fraud detectionComment: 12 pages, 10 figure

Advanced analytical methods for fraud detection: a systematic literature review

The developments of the digital era demand new ways of producing goods and rendering services. This fast-paced evolution in the companies implies a new approach from the auditors, who must keep up with the constant transformation. With the dynamic dimensions of data, it is important to seize the opportunity to add value to the companies. The need to apply more robust methods to detect fraud is evident. In this thesis the use of advanced analytical methods for fraud detection will be investigated, through the analysis of the existent literature on this topic. Both a systematic review of the literature and a bibliometric approach will be applied to the most appropriate database to measure the scientific production and current trends. This study intends to contribute to the academic research that have been conducted, in order to centralize the existing information on this topic

A systematic review on machine learning models for online learning and examination systems

Examinations or assessments play a vital role in every student’s life; they determine their future and career paths. The COVID pandemic has left adverse impacts in all areas, including the academic field. The regularized classroom learning and face-to-face real-time examinations were not feasible to avoid widespread infection and ensure safety. During these desperate times, technological advancements stepped in to aid students in continuing their education without any academic breaks. Machine learning is a key to this digital transformation of schools or colleges from real-time to online mode. Online learning and examination during lockdown were made possible by Machine learning methods. In this article, a systematic review of the role of Machine learning in Lockdown Exam Management Systems was conducted by evaluating 135 studies over the last five years. The significance of Machine learning in the entire exam cycle from pre-exam preparation, conduction of examination, and evaluation were studied and discussed. The unsupervised or supervised Machine learning algorithms were identified and categorized in each process. The primary aspects of examinations, such as authentication, scheduling, proctoring, and cheat or fraud detection, are investigated in detail with Machine learning perspectives. The main attributes, such as prediction of at-risk students, adaptive learning, and monitoring of students, are integrated for more understanding of the role of machine learning in exam preparation, followed by its management of the post-examination process. Finally, this review concludes with issues and challenges that machine learning imposes on the examination system, and these issues are discussed with solutions

The Role Artificial Intelligence in Modern Banking: An Exploration of AI-Driven Approaches for Enhanced Fraud Prevention, Risk Management, and Regulatory Compliance

Banking fraud prevention and risk management are paramount in the modern financial landscape, and the integration of Artificial Intelligence (AI) offers a promising avenue for advancements in these areas. This research delves into the multifaceted applications of AI in detecting, preventing, and managing fraudulent activities within the banking sector. Traditional fraud detection systems, predominantly rule-based, often fall short in real-time detection capabilities. In contrast, AI can swiftly analyze extensive transactional data, pinpointing anomalies and potentially fraudulent activities as they transpire. One of the standout methodologies includes the use of deep learning, particularly neural networks, which, when trained on historical fraud data, can discern intricate patterns and predict fraudulent transactions with remarkable precision.  Furthermore, the enhancement of Know Your Customer (KYC) processes is achievable through Natural Language Processing (NLP), where AI scrutinizes textual data from various sources, ensuring customer authenticity. Graph analytics offers a unique perspective by visualizing transactional relationships, potentially highlighting suspicious activities such as rapid fund transfers indicative of money laundering. Predictive analytics, transcending traditional credit scoring methods, incorporates a diverse data set, offering a more comprehensive insight into a customer's creditworthiness.  The research also underscores the importance of user-friendly interfaces like AI-powered chatbots for immediate reporting of suspicious activities and the integration of advanced biometric verifications, including facial and voice recognition. Geospatial analysis and behavioral biometrics further bolster security by analyzing transaction locations and user interaction patterns, respectively.  A significant advantage of AI lies in its adaptability. Self-learning systems ensure that as fraudulent tactics evolve, the AI mechanisms remain updated, maintaining their efficacy. This adaptability extends to phishing detection, IoT integration, and cross-channel analysis, providing a comprehensive defense against multifaceted fraudulent attempts. Moreover, AI's capability to simulate economic scenarios aids in proactive risk management, while its ability to ensure regulatory compliance automates and streamlines a traditionally cumbersome process

IMPROVING CREDIT CARD FRAUD DETECTION USING TRANSFER LEARNING AND DATA RESAMPLING TECHNIQUES

This Culminating Experience Project explores the use of machine learning algorithms to detect credit card fraud. The research questions are: Q1. What cross-domain techniques developed in other domains can be effectively adapted and applied to mitigate or eliminate credit card fraud, and how do these techniques compare in terms of fraud detection accuracy and efficiency? Q2. To what extent do synthetic data generation methods effectively mitigate the challenges posed by imbalanced datasets in credit card fraud detection, and how do these methods impact classification performance? Q3. To what extent can the combination of transfer learning and innovative data resampling techniques improve the accuracy and efficiency of credit card fraud detection systems when dealing with imbalanced datasets, and what novel strategies can be developed to address this common challenge? The main findings are: Q1. Unconventional cross-domain methods improved fraud detection, holding promise for enhanced security. Q2. The problems caused by unbalanced datasets in credit card fraud detection were effectively addressed by the synthetic data generation techniques SMOTE and ADASYN, resulting in a more balanced dataset suitable for fraud classification. Q3. The combination of neural networks and data resampling techniques, such as SMOTE and ADASYN, significantly improved credit card fraud detection accuracy. The main conclusions are: Q1. Cross-domain methods are useful for credit card fraud detection, especially when it comes to online transactions. Q2. When used with various classifiers, neural networks show remarkable accuracy rates: 97% for unbalanced data, 99.47% for SMOTE, and 99.11% for ADASYN Q3. A fraud recall of 0.99 is obtained by the model evaluation on imbalanced data, with 12,155 right predictions out of 12,336 and 181 incorrect ones. The identified areas for further study encompass the testing of our model on larger datasets and the optimization of hyperparameters for further enhancement

Strengthening governance of social safety nets in East Asia

Several East Asian countries, in the aftermath of the global financial crisis, are considering an expansion of their social safety net programs. In many cases, existing delivery mechanisms for social assistance in the region tend to be basic, in line with the small size of programs. In a context of coverage expansion and proliferation of new programs, the risk of creating increasingly complex systems characterized by cross-incentives is high. Lack of coordination, ambiguous criteria for identifying and selecting beneficiaries, low administrative capacity, lack of transparency and limited beneficiary participation pose risks for program effectiveness and can decrease accountability. Good governance can improve program outcomes through effective program coordination, stronger accountability arrangements, provider incentives and greater transparency and participation. This paper proposes an analytical framework to systematically identify governance risks and constraints which, if removed, could improve the outcomes of modern social assistance programs.Health Monitoring&Evaluation,Safety Nets and Transfers,National Governance,Governance Indicators,Poverty Monitoring&Analysis

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