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7 results for “Quantifi”
FinScan adopts AI-powered adverse media screening from Quantifind
FinScan, an AML and sanctions compliance provider, partnered with Quantifind to integrate its AI-powered adverse media screening technology into FinScan’s compliance platform.
Sep 8, 2026
Bayesian methods and Markov chain Monte Carlo algorithms for curve reconstruction and point cloud data analysis
A new Bayesian statistical framework for reconstructing curves from point-cloud data introduces uncertainty quantification to address noise, missing data, and overconfidence in existing reconstruction methods.
Aug 28, 2026
The Changing Geometry of Grammar: Dimensionality and Neighborhood Reorganization across Transformer Layers
A new arXiv preprint analyzes how grammatical roles (e.g., nouns vs. prepositions) shape the geometric structure of transformer token representations across layers, using intrinsic dimensionality and neighborhood analysis to reveal systematic, architecture-dependent reorganization.
Aug 27, 2026
Improved Confidence Estimates for Black-Box Large Language Models
A new arXiv preprint proposes a lightweight, dataset-aware method to improve confidence estimates for black-box LLMs by training simple classifiers on existing uncertainty scores and query similarity — aiming to increase reliability without requiring model access or labeled correctness data.
Aug 21, 2026
Triangular Fuzzy Rescaling Distance
A new fuzzy distance metric called Triangular Fuzzy Rescaling Distance (d_{TR}) is introduced to handle heterogeneous-scale triangular fuzzy numbers without pre-normalization, with formal metric proofs and stated applicability to synthetic indicators and fuzzy ML.
Aug 21, 2026
Bred Banque Populaire selects Quantifi for market risk
Bred Banque Populaire has selected Quantifi’s market risk platform to modernize its risk infrastructure, signaling a vendor adoption in financial risk management.
Jul 13, 2026
Profit-Based Counterfactual Explanations for Product Improvement: A Case Study of Manga Sales in Japan
Researchers propose 'profit-based counterfactual explanations' (PBCE) — a new AI interpretability method that reframes counterfactual generation as profit maximization, replacing arbitrary target values and abstract distance metrics with economically grounded cost and revenue terms.
Published Jul 3, 2026 · Analyzed Jul 6, 2026