The paper introduces Governance-as-Code (GaC), a framework that translates the EU AI Act’s technical requirements into 43 machine‑checkable acceptance criteria across six compliance modules. GaC runs within a CI/CD pipeline, producing Article‑indexed audit evidence and providing actual Rego policy code. The authors validate GaC on two enterprise deployments, showing it reproduces manual audit findings—including three penalty‑triggering violations—while reducing audit labor by about 75%.
By Rudrendu Kumar Paul, Sourav Nandy
arXiv:2607. 16130v1 Announce Type: cross Abstract: AI governance increasingly requires judgments about whether an AI system remains adequately trustworthy over time, whether observed changes are tolerable, and how such judgments should be documented in a transparent and contestable way.
By Andrea Ferrario
The paper discusses how financial institutions are increasingly using AI agents in areas such as credit, fraud, and compliance, yet current governance focuses only on individual components. It introduces ARIA, a finance‑specific reference architecture that adds six capabilities—policy specification, population‑level monitoring, bounded authority, runtime containment, adaptive policy change, and preserved human oversight—to address the gap of constitutional non‑compositionality. Two simulations demonstrate how local controls can miss collective bias and how observed‑versus‑expected monitoring can provide earlier warnings of drift.
By Jose Manuel de la Chica Rodriguez, Juan Manuel Vera Diaz, Pablo Delgado Romero
Financial institutions are beginning to deploy agentic workflows in credit, fraud, collections, compliance, and operational control. Governance remains largely component-centric: each model or agent i...
arXiv:2607. 17797v1 Announce Type: new Abstract: Financial statements (FS) such as Balance Sheet (BS), Income Statement (IS) and Cash-flow Statement (CS) summarize the annual financial performance of a company.
By Kshitij Madhav Jadhav, Sushodhan Vaishampayan, Manoj Apte, Sachin Pawar, Nitin Ramrakhiyani, Girish Keshav Palshikar
arXiv:2608. 11344v1 Announce Type: cross Abstract: Financial institutions are delegating consequential decisions to agentic AI systems that decompose goals, coordinate models and tools, and act with little oversight.
By Henry Han
arXiv:2607. 19292v1 Announce Type: cross Abstract: Current AI safety discourse still focuses disproportionately on visible failures, including obvious harms, dramatic misuse, and hypothetical catastrophic scenarios.
By Gjergji Kasneci, Enkelejda Kasneci
arXiv:2605. 16281v2 Announce Type: replace-cross Abstract: Post-deployment accountability has become central to AI governance, yet little empirical evidence shows whether monitoring, incident reporting, and impact assessment obligations are visible when AI systems fail.
By Ummara Mumtaz, Summaya Mumtaz
The paper proposes a framework that connects structured hazard analysis, component-level testing, and probabilistic system modelling to assess system-level harms from AI in complex sociotechnical systems. It demonstrates the approach using the UK's Real Time Gross Settlement system, showing how adversarial inputs to LLM-based trading can shift AI behaviour, reduce system resilience, and increase the likelihood of cascading bank failures. The framework aims to provide a traceable pathway from model behaviour to systemic outcomes, enabling evidence-based governance of AI in critical infrastructure.
By Paul Vautravers, Oliver Chalkley, Gabriel Downer, Kate S, Damian Ruck
The study investigates whether Large Language Models (LLMs) can translate technical explanations from credit risk models into stakeholder-friendly narratives. Using Freddie Mac loan data, the authors compare standard tabular models (XGBoost + SHAP) with alternative data pipelines (GNN + GNNExplainer and a bimodal mix) and generate explanations with three LLM configurations: a small fine‑tuned Gemma 3 4B, a large fine‑tuned DeepSeek R1 70B, and a zero‑shot Gemini 2.5. Findings show that the quality of explanations is more dependent on the evidence representation than on the LLM, that narratives reliably identify influential factors but are less consistent about the direction of influence, and that credit professionals demand higher evidentiary standards than non‑professionals.
By Sahab Zandi, Noah Kostesku, Christophe Mues, Mar\'ia \'Oskarsd\'ottir, Cristi\'an Bravo
arXiv:2606. 14594v1 Announce Type: cross Abstract: AI-assisted software development has moved from line-level autocomplete to agents that can plan changes, edit files, and submit pull requests with limited human supervision.
By Jassem Manita, Aziz Amari
The paper introduces a compliance-first AI architecture for regulated finance, treating regulation as an orientation layer rather than a rigid rule set. It uses a regulatory intent matrix and a governed policy compiler to translate regulatory requirements into concrete prohibitions, obligations, and runtime budgets, while maintaining proportional committee activation and supervisory oversight. Evidence and decisions are recorded on a permissioned DAG with deterministic timestamps, enabling replay, provenance checks, and clear attribution of failures, and clause-level legal indexing ensures portability across the DACH region and the EU.
By Walter Kurz, Reinhard Magg