Fraud Detection in Mobile Payment Systems using an XGBoost-based Framework

2023 | Information Systems Frontiers | Citations: 0

Authors: Hajek, Petr; Abedin, Mohammad Zoynul; Sivarajah, Uthayasankar

Abstract: Mobile payment systems are becoming more popular due to the increase in the numb ... Expand

Abstract: Mobile payment systems are becoming more popular due to the increase in the number of smartphones, which, in turn, attracts the interest of fraudsters. Extant research has therefore developed various fraud detection methods using supervised machine learning. However, sufficient labeled data are rarely available and their detection performance is negatively affected by the extreme class imbalance in financial fraud data. The purpose of this study is to propose an XGBoost-based fraud detection framework while considering the financial consequences of fraud detection systems. The framework was empirically validated on a large dataset of more than 6 million mobile transactions. To demonstrate the effectiveness of the proposed framework, we conducted a comparative evaluation of existing machine learning methods designed for modeling imbalanced data and outlier detection. The results suggest that in terms of standard classification measures, the proposed semi-supervised ensemble model integrating multiple unsupervised outlier detection algorithms and an XGBoost classifier achieves the best results, while the highest cost savings can be achieved by combining random under-sampling and XGBoost methods. This study has therefore financial implications for organizations to make appropriate decisions regarding the implementation of effective fraud detection systems. Collapse

Topics: fraud detection mobile system mobile payment electronic payment Python

Methods: XGBoost machine learning anomaly detection supervised learning experiment

A novel approach for fraudulent reviewer detection based on weighted topic modelling and nearest neighbors with asymmetric Kullback–Leibler divergence

2022 | Decision Support Systems | Citations: 0

Authors: Zhang, Wen; Xie, Rui; Wang, Qiang; Yang, Ye; Li, Jian

Abstract: The task of detecting fraudulent reviewers is of great importance to E-commerce ... Expand

Abstract: The task of detecting fraudulent reviewers is of great importance to E-commerce platforms. Existing research has invested much effort into developing comprehensive features and advanced techniques to detect fraudulent reviewers. However, most of these studies have ignored the data imbalance problem inherent in fraudulent reviewer detection: non-fraudulent reviewers are the majority, while fraudulent reviewers are the minority in real practice. To fill this gap, we propose a novel approach called ImDetector to detect fraudulent reviewers while handling data imbalance based on weighted latent Dirichlet allocation (LDA) and Kullback–Leibler (KL) divergence. Specifically, we develop a weighted LDA model to extract the latent topics of reviewers distributed on the review features. Asymmetric KL divergence is adopted to make the similarity measure between reviewers biased toward the fraudulent minority when using the K-nearest-neighbor for classification. By mapping the reviewers to the latent topics of features derived from the weighted LDA model and measuring the similarities between reviewers using asymmetric KL divergence, the data imbalance problem in fraudulent reviewer detection is alleviated. Extensive experiments on the Yelp.com dataset demonstrate that the proposed ImDetector approach is superior to the state-of-the-art techniques used for fraudulent reviewer detection. We also explain the experimental results and present the managerial implications of this paper. Collapse

Topics: online review fraud detection knowledge representation network effect website

Methods: machine learning experiment nonparametric test Mann Whitney U test supervised learning

Automated mortgage origination delay detection from textual conversations

2021 | Decision Support Systems | Citations: 5

Authors: Brahma, Arin; Goldberg, David M.; Zaman, Nohel; Aloiso, Mariano

Abstract: For modern mortgage firms, the process of setting up and verifying a new loan, k ... Expand

Abstract: For modern mortgage firms, the process of setting up and verifying a new loan, known as origination, is complex and multifaceted. The literature notes that this process is rife with delays that can stunt the firm’s business opportunities, but no modern analytical techniques have been developed to address the problem. In this paper, we suggest the use of text analytic and machine learning techniques to predict likely delays. In collaboration with a large national mortgage firm, we derive a large dataset of transcripts from employees’ communications per­ taining to potential loans. We first use information retrieval to generate an initial list of “seed terms,” or terms most associated with loans that were delayed. We then use an array of machine learning approaches to generate predictive models based upon these seed terms. We find that these approaches are comparable in performance to less interpretable state-of-the-art approaches utilizing word embeddings. The resultant models offer interpretable and high-performing solutions to mitigate the risk of delays through early risk detection. Collapse

Topics: business process management information retrieval Python innovation management decision making

Methods: machine learning natural language processing computational algorithm BERT lasso regression

Feature Generation Using Machine Learning from Large Sparse Financial Data

2019 | Americas Conference on Information Systems | Citations: 0

Authors: Choi, Minjung

Abstract: The modeling of customer features has become a core component in modern financi ... Expand

Abstract: The modeling of customer features has become a core component in modern financial analytics. There are several difficulties in adopting conventional machine learning (ML) methodologies to finance domain: distributional asymmetry in the observations, class imbalance in the training labels, and data sparsity resulting from infrequent occurrence. In this study, we try to address the statistical challenges of financial data. Then, we test feature processing using multiple machine learning approaches in combination with established methods. We evaluate separate feature selection results as part of a prediction pipeline, and show how they differ across models. The empirical implications of the feature transformation and selection on the prediction outcomes are discussed. Collapse

Methods: data transformation experiment computational algorithm random forest classification principal component analysis

Prediction of Airline Delays based on Machine Learning Algorithms

2019 | Americas Conference on Information Systems | Citations: 0

Authors: Dand, Alok; Saeed, Khawaja; Yildirim, Bayram

Abstract: Every year around 20% of all flights are delayed or canceled. The costs of thes ... Expand

Abstract: Every year around 20% of all flights are delayed or canceled. The costs of these events to the airline companies and passengers are in billions of dollars each year. According to a report published in 2010 by Research (NEXTOR), around half of the total cost (direct and indirect) is paid by the passenger. The goal of this study is to predict airline delays using binary supervised and unsupervised machine learning classification algorithms using the US Domestic Flights data from 2015-2017. We use an expanded set of variables some of which are subject to decisional control of the airlines to help improve the predictions. Different performance measures such as prediction accuracy, recall rates, receiver operation characteristics -area under the curve scores were used to evaluate the efficacy of different algorithms. Collapse

Topics: database system fraud detection IT career

Methods: machine learning computational algorithm decision tree classification random forest classification naïve bayes