fraud-detection

This paper introduces StreamFraudNet, a weakly supervised incremental model for detecting phone scams from raw speech, achieving a ROC-AUC of 0.9953 and operating in real-time to provide early warnings during calls.

The article reports on AI-powered dating app scams uncovered by Anthropic, where autonomous AI personas impersonate real people to defraud users.

The article argues that many enterprise AI pilots fail because they use generative models for tasks better suited to discriminative models, highlighting differences in mathematical objectives and update mechanisms.

FraudBench is an executable benchmark for stress-testing policy-grounded banking agents against adaptive fraud attacks, simulating scenarios with tools, mutable state, and internal policy documents.

Stripe's acquisition of OpenRouter is focused on ecosystem-wide AI security and alignment, using OpenRouter's AI traffic data to improve fraud detection and risk management through continuous context feedback loops.

This paper studies the application of AI and machine learning algorithms for recognizing fraudulent banking transactions, proposing preprocessing techniques and comparing models. An artificial neural network and stacked generalization achieve improved AUC scores, with the best result around 0.954.

The author built an MCP tool that lets AI agents retrieve a Google Maps reviewer's full history and metadata for vetting reviews and detecting fraudulent patterns.

SeqLLM is a framework that injects behavioral-sequence modeling into pretrained LLMs while preserving language ability, enabling joint analysis of text and behavior for high-stakes decisions. Deployed at WeChat Pay, it improves merchant screening precision from 92.0% to 97.5% and achieves state-of-the-art results on open recommendation benchmarks.

This paper introduces a hierarchical human-AI triage model for POS fraud detection in Nigerian FinTech, designed to neutralize structural bias that discriminates against rural agents due to infrastructure-related noise. The system uses a calibrated ensemble model, specialist analysts, and senior supervisors to achieve substantive equality of opportunity, reducing the regional performance gap from 19.43 to 2.88 percentage points.

RUBRIC is a generator-agnostic filtering framework for imbalanced classification that selects synthetic samples by balancing realism (via a discriminator) and utility (margin-based scoring), improving F1-macro and recall on benchmarks like credit-card fraud detection.

This paper presents an exploratory benchmark for detecting sparse-ring fraud in dynamic transaction graphs using quantum-inspired Contextual Machine Learning (CML) compared to a GRU baseline, finding that hybrid graph features combining identity-preserving and topological summaries yield the best results.

A comment on how the decision not to forward 'sort by amount received' to fraud departments is always a policy choice, highlighting organizational priorities.

An interactive exploration of Benford's Law across real datasets, explaining the mathematical phenomenon where the digit 1 appears as the first digit about 30% of the time, and its applications in fraud detection.

This paper presents a controlled comparison of seven categorical encoding methods on the IEEE-CIS fraud dataset, evaluating accuracy, interpretability, and computational trade-offs with a fixed LightGBM learner, finding that entity embeddings achieve the highest AUC-ROC but no encoder dominates both metrics.

NVIDIA's blog post describes an end-to-end workflow using PyTorch-native NeMo AutoModel for pretraining a transaction foundation model. The workflow uses GPU-accelerated data processing and tokenization, decoder-only model pretraining, and embedding extraction to improve fraud classification performance by over 40% on the IBM TabFormer dataset.

This paper presents a synthetic multimodal framework for insurance fraud detection at the first notice of loss (FNOL). It generates dialogue transcripts and two-speaker audio, combining ASR, NER, LLM-RAG, and speaker embeddings into a rule-based risk scoring system.

Proposes the Multi-Stream Fraud Transformer (MSFT) for financial fraud detection, which independently encodes transaction, login, and risk event streams using Transformers and fuses them with time-aware positional encoding and gated fusion, achieving 0.9961 AUROC on a large dataset.

Proposes TMR-GGNN, a time-aware multi-relational graph neural network for credit card fraud detection that handles imbalanced data and evolving fraud patterns via contrastive learning and focal loss.

This article from Sumsub examines the rising threat of AI-powered deepfake fraud targeting the creator economy, and offers a guide on detection and protection methods to combat impersonation and voice cloning scams.

A practitioner recounts how deploying AI agents in production required 80% engineering effort on workflow, ownership, and approval processes rather than the model itself, highlighting that the 'boring layer' of shared context and routing is critical for real-world impact.