Given a stream of money transactions between accounts in a bank, how can we accurately detect money laundering agent accounts and suspected behaviors in real-time? Money laundering agents try to hide the origin of illegally obtained money by dispersive multiple small transactions and evade detection by smart strategies. Therefore, it is challenging to accurately catch such fraudsters in an unsupervised manner. Existing approaches do not consider the characteristics of those agent accounts and are not suitable to the streaming settings. Therefore, we propose MonLAD and MonLAD-W to detect money laundering agent accounts in a transaction stream by keeping track of their residuals and other features; we devise AnoScore algorithm to find anomalies based on the robust measure of statistical deviation. Experimental results show that MonLAD outperforms the state-of-the-art baselines on real-world data and finds various suspicious behavior patterns of money laundering. Additionally, several detected suspected accounts have been manually-verified as agents in real money laundering scenario.

/pdf/monlad-money-laundering-agents-detection-in-transaction-2e6f0jbr.pdf

MonLAD: Money Laundering Agents Detection in Transaction Streams

Sustainability has become the greatest aspiration on the planet, as every organization has begun reorienting itself to become one. To accomplish this, organizations must strike a balance between their Environmental, Social, and Governance (ESG) policies. It is essential to examine the organizations that currently exist. For this reason, the study conducts a thematic analysis of the sustainability reports of Forbes’ India’s top companies in 2021 in order to determine their current ESG focus using BERT tokenization and YAKE technique along with MLM and NSP methods of natural language processing (NLP). The experiment results in prediction with 98% accuracy. The study addresses the question, “What must organizations do to embark on the path towards sustainability?”

A Natural Language Processing Model on BERT and YAKE Technique for Keyword Extraction on Sustainability Reports

Given a stream of graph edges from a dynamic graph, how can we assign anomaly scores to edges and subgraphs in an online manner, for the purpose of detecting unusual behavior, using constant time and memory? For example, in intrusion detection, existing work seeks to detect either anomalous edges or anomalous subgraphs, but not both. In this paper, we first extend the count-min sketch data structure to a higher-order sketch. This higher-order sketch has the useful property of preserving the dense subgraph structure (dense subgraphs in the input turn into dense submatrices in the data structure). We then propose four online algorithms that utilize this enhanced data structure, which (a) detect both edge and graph anomalies; (b) process each edge and graph in constant memory and constant update time per newly arriving edge, and; (c) outperform state-of-the-art baselines on four real-world datasets. Our method is the first streaming approach that incorporates dense subgraph search to detect graph anomalies in constant memory and time.

Sketch-Based Streaming Anomaly Detection in Dynamic Graphs.

Crowdfunding platforms are important for startups, since they offer diverse financing options, market validation, and promotional opportunities through an investor community. These platforms provide detailed company information, aiding informed investment decisions within a regulated and secure environment. Machine learning (ML) techniques are important in analyzing large data sets, detecting anomalies and fraud, and enhancing decision-making and business strategies. A systematic review employed PRISMA guidelines, which studied how ML improves fraud detection on digital crowdfunding platforms. The analysis includes English-language studies from peer-reviewed journals published between 2018 and 2023 to analyze the pre- and post-COVID-19 pandemic. The findings indicate that ML techniques such as Random Forest, Support Vector Machine, and Artificial Neural Networks significantly enhance the predictive accuracy and utility of tax planning for startups considering equity crowdfunding. The United States, Germany, Canada, Italy, and Turkey do not present statistically significant differences at the 95% confidence level, standing out for their notable academic visibility. Florida Atlantic and Cornell Universities, Springer and John Wiley &amp; Sons Ltd. publishing houses, and the Journal of Business Ethics and Management Science magazines present the highest citations without statistical differences at the 95% confidence level. 

Bibliometric Analysis of the Machine Learning Applications in Fraud Detection on Crowdfunding Platforms

With the rapid development of electronic payment, risks in large-scale financial transaction networks are persistently arising. One important problem is how to use the transaction data to detect sellers who do fraudulent trades on the online platform. This paper analyzes three attributes of the transaction flow: money (what), time (when), region (where), and summarizes corresponding characteristics among fraudulent sellers. Based on this, we propose an unsupervised anomaly detection algorithm: W3Detector. It processes the raw data with a new sequence discretization method and uses statistical tools to convert data indicators into information value. With the minimum description length principle, we finally filter out the suspicious sellers. The algorithm is not affected by factors such as unit or range of the attributes, and is highly flexible and scalable. After running on three real-world transaction datasets collected from WeChat Pay, W3Detector has show significantly better performance than the baseline methods of anomaly detection, with weighted accuraccy (WACC) increased by 1.13X and F-score increased by 0.105 on average, and the recall value over 60%.

W3Detector: Detecting Fraudulent Online Sellers Based on Temporal and Spacial Information

How can we track synchronized behavior in a stream of time-stamped tuples, such as mobile devices installing and uninstalling applications in the lockstep, to boost their ranks in the app store? We model such tuples as entries in a streaming tensor, which augments attribute sizes in its modes over time. Synchronized behavior tends to form dense blocks (i.e. subtensors) in such a tensor, signaling anomalous behavior, or interesting communities. However, existing dense block detection methods are either based on a static tensor, or lack an efficient algorithm in a streaming setting. Therefore, we propose a fast streaming algorithm, AugSplicing, which can detect the top dense blocks by incrementally splicing the previous detection with the incoming ones in new tuples, avoiding re-runs over all the history data at every tracking time step. AugSplicing is based on a splicing condition that guides the algorithm (Section 4). Compared to the state-of-the-art methods, our method is (1) effective to detect fraudulent behavior in installing data of real-world apps and find a synchronized group of students with interesting features in campus Wi-Fi data; (2) robust with splicing theory for dense block detection; (3) streaming and faster than the existing streaming algorithm, with closely comparable accuracy.

pdf/augsplicing-synchronized-behavior-detection-in-streaming-3jyy8057ja.pdf

AugSplicing: Synchronized Behavior Detection in Streaming Tensors

How can we track synchronized behavior in a stream of time-stamped tuples, such as mobile devices installing and uninstalling applications in the lockstep, to boost their ranks in the app store? We model such tuples as entries in a streaming tensor, which augments attribute sizes in its modes over time. Synchronized behavior tends to form dense blocks (i.e.~subtensors) in such a tensor, signaling anomalous behavior, or interesting communities. However, existing dense block detection methods are either based on a static tensor, or lack an efficient algorithm in a streaming setting. Therefore, we propose a fast streaming algorithm, AUGSPLICING, which can detect the top dense blocks by incrementally splicing the previous detection with the incoming ones in new tuples, avoiding re-runs over all the history data at every tracking time step. AUGSPLICING is based on a splicing condition that guides the algorithm (Section 4). Compared to the state-of-the-art methods, our method is (1) effective to detect fraudulent behavior in installing data of real-world apps and find a synchronized group of students with interesting features in campus Wi-Fi data; (2) robust with splicing theory for dense block detection; (3) streaming and faster than the existing streaming algorithm, with closely comparable accuracy.

Despite the prevalence of scams in crowdfunding projects, currently, there is little research into the identification of fraudulent or infeasible crowdfunding projects. Since detecting fraudulent crowdfunding projects is challenging, most existing research on fake information has focused on detecting fake news or fake charity crowdfunding projects based on social media, but research on fraudulent or infeasible crowdfunding projects is very limited. Therefore, to solve this problem, we focus on how to detect fraudulent crowdfunding projects based on knowledge extraction and contradiction detection. We proposed a novel method called BM5-SP-SC (BERT-MT5-Sentence Pattern-Sentiment Classification). BM5 (BERT-MT5), which is built from a combination of a key-BERT and a fine-tuned MT5 transformers, was used to extract feature information from crowdfunding projects. We proposed a novel method for MT5 training to construct an adaptive BM5 model. The correct rate of keywords extracted by our novel adaptive BM5 model was up to 72.7%, the recall was 100%, and the F-measure was up to 84.19%. The minimum train loss of the BM5 model was up to 0.1342, and the evaluation loss achieved was 0.3064. The BLEU score of summary-to-keyword was 37.336. Moreover, we proposed an SP (Sentence Pattern) matching method to achieve knowledge extraction. Furthermore, SC (Sentiment Classification) was used to build a sentiment classifier thesaurus for identifying fraudulent and infeasible crowdfunding projects. Our proposed BM5-SP-SC achieved an overall accuracy of 85.26% in detecting fraudulent crowdfunding projects.

/pdf/bm5-sp-sc-a-dual-model-architecture-for-contradiction-3e11v2t3.pdf

BM5-SP-SC: A Dual Model Architecture for Contradiction Detection on Crowdfunding Projects

Most of the existing research on contradiction focuses on detecting social media-based fake news or fake charity crowdfunding projects, but research on fake or infeasible crowdfunding projects is very limited. Therefore, we focus on how to gain an understanding of fake crowdfunding projects based on keyword extraction and selection. We used 12 features to extract keywords from web based information about crowdfunding projects. We evaluate this method with crowdfunding projects. Experiments showed that BERT with DT provide results of 69.33% in F-measure, which is better than other algorithms.

Web based keyword extraction on crowdfunding projects with novel features

Fast Track: Synchronized Behavior Detection in Streaming Tensors

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Papers

AugSplicing: Synchronized Behavior Detection in Streaming Tensors

AugSplicing: Synchronized Behavior Detection in Streaming Tensors

BM5-SP-SC: A Dual Model Architecture for Contradiction Detection on Crowdfunding Projects

Web based keyword extraction on crowdfunding projects with novel features

Fast Track: Synchronized Behavior Detection in Streaming Tensors