The article recounts a final‑year project in which the author trained six different models for fraud detection. It highlights the discrepancy between the model that performed best on evaluation metrics and the one that was ultimately chosen for production. The piece reflects on how real‑world constraints can override purely statistical performance.
The article explains that when multiple AI models are asked the same question and their answers are aggregated by voting, the resulting opinions are often less independent than the number of models suggests. It presents a formula that estimates the effective number of independent models and validates this with six open models evaluated on 1,500 exam questions.
How a single evaluation choice inflated my results by 25 points, and what rebuilding honestly taught me about ML systems people might depend on The post My Fall-Detection Model Scored 94%, and It Was Lying to Me appeared first on Towards Data Science .
LLMs are now proposed for fraud detection, scam investigation, content moderation, and other trust-and-safety workflows. Much of the public literature still evaluates them as models, with less attention to their behavior as components in operational pipelines.
The article discusses how model validation standards are evolving for large language model (LLM) based systems, particularly in the banking sector. It examines what aspects of traditional validation break down, what elements remain applicable, and how to effectively test the quality of LLM outputs. The piece offers practical insights into adapting validation playbooks for generative AI applications.
arXiv:2607. 13078v1 Announce Type: cross Abstract: LLMs are now proposed for fraud detection, scam investigation, content moderation, and other trust-and-safety workflows.
arXiv:2606. 03305v1 Announce Type: new Abstract: Benchmark contamination, where evaluation examples appear in a model's training data, threatens the validity of LLM assessment.
arXiv:2607. 10317v1 Announce Type: cross Abstract: Algorithmic decision systems in financial services often rely on data proxies that inadvertently encode structural inequalities.
The study introduces an inductive link‑prediction framework for detecting payroll fraud on synthetic HR/ERP graphs, comparing analyst rules, path‑feature models, and graph neural networks (GNNs). A gradient‑boosted model using chained‑join features achieved the highest link average precision (AP 0.823), outperforming a GraIL‑style GNN (AP 0.663) and demonstrating that multi‑hop gains are not exclusive to GNNs. In additional experiments, the chained‑join model again led on a new cohort (AP 0.798), while the relational GNN surpassed record‑aware rules (AP 0.707 vs. 0.504) under various conditions, highlighting the distinct roles of multi‑hop discovery, record‑based discrimination, and review prioritization.
arXiv:2607. 19266v1 Announce Type: cross Abstract: Fraud detection systems must scale with rising transaction volume while remaining explainable and reviewable.
The article compares seven open decision models with a generative large language model (LLM) in a hands‑on repository called Jev. It evaluates each model based on routing accuracy, latency, and output reliability. The comparison provides practical insights into how these models perform in real‑world scenarios.
arXiv:2608. 02786v1 Announce Type: new Abstract: AI systems can fail silently.
A practical guide to building an evaluation workflow that catches retrieval failures, hallucinations, and performance drift before they reach users The post Building Trustworthy Production RAG Systems Through Continuous Evaluation appeared first on Towards Data Science .