Identity theft involving stolen driver's licenses creates lasting legal exposure unlike credit card fraud, as license numbers cannot be changed and require extensive cleanup efforts.

As deepfake video technology improves, the scale of online fraud will grow even further, experts say. As deepfake video technology improves, the scale of online fraud will grow even further, experts say. AI content for scams can be targeted at individuals and'produced by pretty much anybody', researchers say Deepfake fraud has gone "industrial", an analysis published by AI experts has said. Tools to create tailored, even personalised, scams - leveraging, for example, deepfake videos of Swedish journalists or the president of Cyprus - are no longer niche, but inexpensive and easy to deploy at scale, said the analysis from the AI Incident Database . These examples are part of a trend in which scammers are using widely available AI tools to perpetuate increasingly targeted heists.

Things to Do in L.A. Tim Walz isn't the only governor plagued by fraud. Minnesota Gov. Tim Walz said he would not seek a third term amid attacks over a fraud scandal involving child care funding. This is read by an automated voice. Please report any issues or inconsistencies here . California has lost billions to cheats in the last few years, leaving Newsom vulnerable to the same sort of attack that took down Walz.

Everlane, Revolve and Under Armour pilot Happy Returns' AI system to combat return fraud as retailers prepare for $850 billion in returned goods in 2025.

The decentralized finance (DeFi) community has grown rapidly in recent years, pushed forward by cryptocurrency enthusiasts interested in the vast untapped potential of new markets. The surge in popularity of cryptocurrency has ushered in a new era of financial crime. Unfortunately, the novelty of the technology makes the task of catching and prosecuting offenders particularly challenging. Thus, it is necessary to implement automated detection tools related to policies to address the growing criminality in the cryptocurrency realm.

Abstract--Tree-based ensemble methods consistently outperform single models on tabular classification tasks, yet the conditions under which ensembles provide clear advantages--and prevent overfitting despite using high-variance base learners--are not always well understood by practitioners. We study four real-world classification problems (Breast Cancer diagnosis, Heart Disease prediction, Pima Indians Diabetes, and Credit Card Fraud detection) comparing classical single models against nine ensemble methods using five-seed repeated stratified cross-validation with statistical significance testing. Our results reveal three distinct regimes: (i) On nearly linearly separable data (Breast Cancer), well-regularized linear models achieve 97% accuracy with <2% generalization gaps; ensembles match but do not substantially exceed this performance. We systematically quantify dataset complexity through linearity scores, feature correlation, class separability, and noise estimates, explaining why different data regimes favor different model families. Cross-validated train/test accuracy and generalization-gap plots provide simple visual diagnostics for practitioners to assess when ensemble complexity is warranted. Statistical testing confirms that ensemble gains are significant on nonlinear tasks (p < 0.01) but not on near-linear data (p > 0.15). The study provides actionable guidelines for ensemble model selection in high-stakes tabular applications, with full code and reproducible experiments publicly available. A model that almost perfectly fits its training data can still fail badly on new cases. This gap between training performance and real-world behaviour is the essence of overfitting, and it is particularly problematic in domains such as medical diagnosis and financial fraud detection, where mistakes are costly: missed tumours delay treatment, and undetected fraud translates directly into monetary loss.

This work presents a review of attack methodologies targeting Pix, the instant payment system launched by the Central Bank of Brazil in 2020. The study aims to identify and classify the main types of fraud affecting users and financial institutions, highlighting the evolution and increasing sophistication of these techniques. The methodology combines a structured literature review with exploratory interviews conducted with professionals from the banking sector. The results show that fraud schemes have evolved from purely social engineering approaches to hybrid strategies that integrate human manipulation with technical exploitation. The study concludes that security measures must advance at the same pace as the growing complexity of attack methodologies, with particular emphasis on adaptive defenses and continuous user awareness.

In this work, we study the risks of collective financial fraud in large-scale multi-agent systems powered by large language model (LLM) agents. We investigate whether agents can collaborate in fraudulent behaviors, how such collaboration amplifies risks, and what factors influence fraud success. To support this research, we present MultiAgentFraudBench, a large-scale benchmark for simulating financial fraud scenarios based on realistic online interactions. The benchmark covers 28 typical online fraud scenarios, spanning the full fraud lifecycle across both public and private domains. We further analyze key factors affecting fraud success, including interaction depth, activity level, and fine-grained collaboration failure modes. Finally, we propose a series of mitigation strategies, including adding content-level warnings to fraudulent posts and dialogues, using LLMs as monitors to block potentially malicious agents, and fostering group resilience through information sharing at the societal level. Notably, we observe that malicious agents can adapt to environmental interventions. Our findings highlight the real-world risks of multi-agent financial fraud and suggest practical measures for mitigating them. Code is available at https://github.com/zheng977/MutiAgent4Fraud.

Generative AI is supercharging insurance fraud by making it easier to falsify accident evidence at scale and in rapid time. Insurance fraud is a pervasive and costly problem, amounting to tens of billions of dollars in losses each year. In the vehicle insurance sector, fraud schemes have traditionally involved staged accidents, exaggerated damage, or forged documents. The rise of generative AI, including deepfake image and video generation, has introduced new methods for committing fraud at scale. Fraudsters can now fabricate highly realistic crash photos, damage evidence, and even fake identities or documents with minimal effort, exploiting AI tools to bolster false insurance claims. Insurers have begun deploying countermeasures such as AI-based deepfake detection software and enhanced verification processes to detect and mitigate these AI-driven scams. However, current mitigation strategies face significant limitations. Detection tools can suffer from false positives and negatives, and sophisticated fraudsters continuously adapt their tactics to evade automated checks. This cat-and-mouse arms race between generative AI and detection technology, combined with resource and cost barriers for insurers, means that combating AI-enabled insurance fraud remains an ongoing challenge. In this white paper, we present UVeye layered solution for vehicle fraud, representing a major leap forward in the ability to detect, mitigate and deter this new wave of fraud.

Graph-based methods are becoming increasingly popular in machine learning due to their ability to model complex data and relations. Insurance fraud is a prime use case, since false claims are often the result of organised criminals that stage accidents or the same persons filing erroneous claims on multiple policies. One challenge is that graph-based approaches struggle to find meaningful representations of the data because of the high class imbalance present in fraud data. Another is that insurance networks are heterogeneous and dynamic, given the changing relations among people, companies and policies. That is why gradient boosted tree approaches on tabular data still dominate the field. Therefore, we present a novel inductive graph gradient boosting machine (G-GBM) for supervised learning on heterogeneous and dynamic graphs. We show that our estimator competes with popular graph neural network approaches in an experiment using a variety of simulated random graphs. We demonstrate the power of G-GBM for insurance fraud detection using an open-source and a real-world, proprietary dataset. Given that the backbone model is a gradient boosting forest, we apply established explainability methods to gain better insights into the predictions made by G-GBM.