Fraud Detection

Designing an attack-defense game: how to increase robustness of financial transaction models via a competition ArXiv ID: 2308.11406 “View on arXiv” Authors: Unknown Abstract Banks routinely use neural networks to make decisions. While these models offer higher accuracy, they are susceptible to adversarial attacks, a risk often overlooked in the context of event sequences, particularly sequences of financial transactions, as most works consider computer vision and NLP modalities. We propose a thorough approach to studying these risks: a novel type of competition that allows a realistic and detailed investigation of problems in financial transaction data. The participants directly oppose each other, proposing attacks and defenses – so they are examined in close-to-real-life conditions. The paper outlines our unique competition structure with direct opposition of participants, presents results for several different top submissions, and analyzes the competition results. We also introduce a new open dataset featuring financial transactions with credit default labels, enhancing the scope for practical research and development. ...

Realistic Synthetic Financial Transactions for Anti-Money Laundering Models ArXiv ID: 2306.16424 “View on arXiv” Authors: Unknown Abstract With the widespread digitization of finance and the increasing popularity of cryptocurrencies, the sophistication of fraud schemes devised by cybercriminals is growing. Money laundering – the movement of illicit funds to conceal their origins – can cross bank and national boundaries, producing complex transaction patterns. The UN estimates 2-5% of global GDP or $0.8 - $2.0 trillion dollars are laundered globally each year. Unfortunately, real data to train machine learning models to detect laundering is generally not available, and previous synthetic data generators have had significant shortcomings. A realistic, standardized, publicly-available benchmark is needed for comparing models and for the advancement of the area. To this end, this paper contributes a synthetic financial transaction dataset generator and a set of synthetically generated AML (Anti-Money Laundering) datasets. We have calibrated this agent-based generator to match real transactions as closely as possible and made the datasets public. We describe the generator in detail and demonstrate how the datasets generated can help compare different machine learning models in terms of their AML abilities. In a key way, using synthetic data in these comparisons can be even better than using real data: the ground truth labels are complete, whilst many laundering transactions in real data are never detected. ...

Abnormal Trading Detection in the NFT Market ArXiv ID: 2306.04643 “View on arXiv” Authors: Unknown Abstract The Non-Fungible-Token (NFT) market has experienced explosive growth in recent years. According to DappRadar, the total transaction volume on OpenSea, the largest NFT marketplace, reached 34.7 billion dollars in February 2023. However, the NFT market is mostly unregulated and there are significant concerns about money laundering, fraud and wash trading. The lack of industry-wide regulations, and the fact that amateur traders and retail investors comprise a significant fraction of the NFT market, make this market particularly vulnerable to fraudulent activities. Therefore it is essential to investigate and highlight the relevant risks involved in NFT trading. In this paper, we attempted to uncover common fraudulent behaviors such as wash trading that could mislead other traders. Using market data, we designed quantitative features from the network, monetary, and temporal perspectives that were fed into K-means clustering unsupervised learning algorithm to sort traders into groups. Lastly, we discussed the clustering results’ significance and how regulations can reduce undesired behaviors. Our work can potentially help regulators narrow down their search space for bad actors in the market as well as provide insights for amateur traders to protect themselves from unforeseen frauds. ...

Fraud Detection and Expected Returns ArXiv ID: ssrn-1998387 “View on arXiv” Authors: Unknown Abstract An accounting-based model has strong out-of-sample power not only to detect fraud, but also to predict cross-sectional returns. Firms with a higher probabilit Keywords: Accounting-Based Models, Fraud Detection, Cross-Sectional Returns, Predictive Analytics, Financial Statement Analysis, Equity Complexity vs Empirical Score Math Complexity: 4.0/10 Empirical Rigor: 7.0/10 Quadrant: Street Traders Why: The paper uses an accounting-based predictive model (high empirical data focus) with statistical validation and out-of-sample testing, but the mathematics described are primarily regression-based and do not involve advanced calculus or complex theoretical derivations. flowchart TD A["Research Goal: Does an accounting-based model<br>predict fraud AND future returns?"] --> B["Methodology: Predictive Analytics<br>Logistic Regression & Cross-Validation"] B --> C["Data Inputs:<br>Financial Statements & Stock Returns"] C --> D["Computational Process:<br>Estimate Prob(Fraud) using Accounting Ratios"] D --> E{"Key Findings"} E --> F["Strong Out-of-Sample Fraud Detection"] E --> G["Predict Cross-Sectional Returns"]