Knowledge catalogue
arXiv:2606.03777v1 Announce Type: new Abstract: AI losses that arise through an insured organization's generative or agentic AI system require state reconstruction, not merely event reconstruction, be
arXiv:2606.16663v2 Announce Type: replace Abstract: Money laundering through insurance claims poses a threat to insurers both through fraudulent payouts and reputational and regulatory risk. Despite t
arXiv:2606.25375v1 Announce Type: new Abstract: With the rapid adoption of generative AI, synthetic medical images pose growing risks, including diagnostic deception and insurance fraud. Although prio
arXiv:2607.13230v1 Announce Type: new Abstract: Agentic AI introduces new insurance challenges because autonomous AI systems can make decisions, invoke tools, modify external environments, and interac
arXiv:2606.05449v1 Announce Type: new Abstract: Agentic artificial intelligence (AI) systems are transforming the risk landscape by extending beyond information generation to autonomous planning, tool
arXiv:2609.33376v1 Announce Type: cross Abstract: Fraud detection is an important area of research in the insurance business due to its financial implications. The primary aim of a fraud detection mod
arXiv:2510.05676v2 Announce Type: replace Abstract: Graph-based methods are becoming increasingly popular in machine learning due to their ability to model complex data and relations. Insurance fraud
arXiv:2610.09671v1 Announce Type: new Abstract: Recent advances in reasoning-oriented large language models (LLMs) have motivated increasing evaluation of their ability to perform professional decisio
arXiv:2609.30765v1 Announce Type: new Abstract: Insurance reserve estimation is a fundamental actuarial task supporting premium pricing, solvency assessment, financial reporting, capital planning, and
arXiv:2609.01188v1 Announce Type: new Abstract: Large Language Models (LLMs) are revolutionizing digital communication by powering conversational agents deployed across domains such as customer servic
arXiv:2503.21321v2 Announce Type: replace-cross Abstract: With the rapid development of machine learning and deep learning techniques, actuaries and the broader insurance industry face a persistent tr
arXiv:2603.14463v2 Announce Type: replace Abstract: Adapting Large Language Models (LLMs) to high-stakes vertical domains like insurance presents a significant challenge: scenarios demand strict adher
arXiv:2605.28327v1 Announce Type: cross Abstract: Traditional insurance pricing relies on risk-based principles that ensure actuarial fairness and solvency but do not explicitly account for policyhold
arXiv:2605.22892v1 Announce Type: cross Abstract: Modelling claim frequency and severity for non-life insurance pricing predominantly relies on generalised linear models, with gradient-boosted machine
arXiv:2609.37454v1 Announce Type: new Abstract: Underwriters in commercial Property and Casualty (P&C) insurance spend 30 to 40% of their time on administrative work rather than risk judgment, and a s
arXiv:2601.12178v2 Announce Type: replace Abstract: We propose a federated learning framework for the calibration of parametric insurance indices under heterogeneous renewable energy production losses
arXiv:2602.13213v2 Announce Type: replace Abstract: Commercial insurance underwriting is a labor-intensive process that requires manual review of extensive documentation to assess risk and determine p
arXiv:2606.28002v1 Announce Type: cross Abstract: Insurance fraud imposes substantial financial losses and operational inefficiencies, raising premiums and impacting trust among legitimate policyholde
arXiv:2606.28048v1 Announce Type: cross Abstract: Insurance fraud remains costly and operationally difficult, particularly in call-centre workflows where many customer interactions begin at FNOL. Whil
arXiv:2606.11463v1 Announce Type: cross Abstract: Accurate loss reserving is foundational to insurer solvency, yet accelerating climate driven catastrophes systematically violate the stability assumpt
arXiv:2605.06678v1 Announce Type: new Abstract: According to the United Nations Office for Disaster Risk Reduction (2025), the average annual cost of natural catastrophes increased from 70--80 billion
arXiv:2605.16699v1 Announce Type: new Abstract: Capped-usage SaaS products -- LLM subscriptions such as Claude Code and ChatGPT, cloud platforms such as Vercel and Cloudflare Workers, corporate benefi
arXiv:2605.18784v1 Announce Type: cross Abstract: The rapid diffusion of agentic AI has created a new coverage problem for commercial insurance: some AI-mediated losses are now affirmatively insured,
arXiv:2606.16465v2 Announce Type: replace Abstract: AI agents can now take irreversible actions in operational systems, but agent-caused losses are still not clearly assigned, priced, or transferred.
arXiv:2605.25632v1 Announce Type: new Abstract: Autonomous AI agents increasingly issue side-effect-bearing actions: database mutations, refunds, payments, external commitments. We propose the Actuari
arXiv:2507.21112v4 Announce Type: replace Abstract: With the rapid rise of InsurTech, traditional insurance companies are increasingly exploring alternative data sources and advanced technologies to s
arXiv:2608.09650v1 Announce Type: cross Abstract: Reranking medical procedures against patient queries is a critical component of health insurance information retrieval, complicated by a substantial l
arXiv:2601.00911v3 Announce Type: replace-cross Abstract: Automated negotiations in insurance and business-to-business (B2B) commerce encounter substantial challenges. Current systems force a trade-of
arXiv:2609.26445v1 Announce Type: cross Abstract: This manuscript formalizes the most popular model validation tools used in general insurance actuarial modeling. These include graphical tools like ca
arXiv:2609.24927v1 Announce Type: new Abstract: Personal AI agents make recommendations and take actions on people's behalf in high-stakes economic contexts, e.g., buying a flight, choosing health ins
arXiv:2609.23953v1 Announce Type: new Abstract: AI agents that carry a multi-step computer task through on their own became ordinary tools in the past year, and the same autonomy is available to anyon
arXiv:2609.19748v1 Announce Type: new Abstract: In many application domains, such as student dropout, insurance fraud, loan approval, and machine failures, several labelled public datasets are availab
arXiv:2608.28663v1 Announce Type: new Abstract: Reliable individual cattle identification supports disease surveillance, vaccination records, breeding management, and livestock insurance. Although the
arXiv:2608.21998v1 Announce Type: new Abstract: Regional climate risk assessment is critical for applications such as infrastructure design, disaster forecasting, and insurance resource allocation. Ho
arXiv:2607.25468v1 Announce Type: new Abstract: In picture-based agricultural insurance for smallholder farmers, missed damage detections carry substantially higher cost than false alarms: a farmer wh
arXiv:2606.07582v1 Announce Type: cross Abstract: Customer churn prediction is essential across data-driven industries such as insurance, digital banking, eCommerce, and subscription platforms, where
arXiv:2606.06830v1 Announce Type: cross Abstract: Data-driven pricing is increasingly prevalent in sectors such as airlines, lending, insurance, and retail. By learning demand models from customer fea
arXiv:2605.02593v1 Announce Type: new Abstract: Risk scores are an interpretable and actionable class of machine learning models with applications in medicine, insurance, and risk management. Unlike m
arXiv:2604.26188v1 Announce Type: new Abstract: The growing reliance of machine learning models in high-stakes, highly regulated domains such as finance and insurance has created a growing tension bet
arXiv:2604.24597v1 Announce Type: cross Abstract: We provide evidence of quantum kernel advantage under noiseless simulation in binary insurance classification on MIMIC-CXR chest radiographs using qua
arXiv:2604.21893v1 Announce Type: cross Abstract: Geographic context is often consider relevant to motor insurance risk, yet public actuarial datasets provide limited location identifiers, constrainin
arXiv:2609.14744v1 Announce Type: new Abstract: By acquiring compute, credentials, accounts, services, and other agents, autonomous AI agents can introduce new authority into a task. Payment, budget,
arXiv:2511.17954v2 Announce Type: replace-cross Abstract: Incorporating spatial information, particularly when related to climate, weather, and demographic factors, is crucial for improving underwriti
arXiv:2508.06692v3 Announce Type: replace Abstract: Federated learning systems typically allocate gradient compression by link speed. This is sensible when bandwidth and data informativeness align. Ho
arXiv:2608.16909v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly integrated into financial advisory systems, yet their role in reproducing religious bias remains underex
arXiv:2608.06609v1 Announce Type: new Abstract: Automated item evaluation (AIE) refers to the use of computational methods to assess item quality without requiring manual expert review or field testin
arXiv:2607.23386v1 Announce Type: new Abstract: We document a failure class in frontier large language models -- exception chain collapse -- observed in eligibility evaluation under nested conditional
arXiv:2607.19811v1 Announce Type: new Abstract: The Segment Anything Model 2 (SAM2) has advanced temporal promptable segmentation, yet its deployment remains hindered by heavy memory cross-attention o
arXiv:2606.30650v1 Announce Type: cross Abstract: Educational support services often face a qualified-capacity problem: staff time is scarce, qualifications decay, new support needs can appear before
arXiv:2604.27891v1 Announce Type: new Abstract: Agent orchestration frameworks -- LangGraph, CrewAI, Google ADK, OpenAI Agents SDK, and others -- place an external orchestrator above the LLM, tracking
arXiv:2604.00555v2 Announce Type: replace Abstract: Enterprise adoption of Large Language Models (LLMs) is constrained by hallucination, domain drift, and the inability to enforce regulatory complianc
arXiv:2604.10853v1 Announce Type: new Abstract: Task-oriented evaluation of knowledge graph (KG) quality increasingly asks whether an ontology-based representation can answer the competency questions
arXiv:2610.04946v1 Announce Type: new Abstract: Cross-system transfer of longitudinal Electronic Health Record (EHR) representations is challenging because clinical coding, patient populations, and he
arXiv:2610.01645v1 Announce Type: new Abstract: Fairness conclusions in clinical prediction can depend strongly on both the metrics reported and the demographic resolution at which performance is eval
arXiv:2609.39486v1 Announce Type: new Abstract: Estimating accident mechanics from real-world crashes is important for vehicle-safety analysis, injury modeling, crash-severity prediction, and operatio
arXiv:2609.23970v1 Announce Type: cross Abstract: We develop exponential family synthetic controls (EFSC), a distributional version of synthetic controls for a panel of datasets. Each cell of the pane
arXiv:2609.16493v1 Announce Type: new Abstract: Traditional catastrophe (CAT) risk models rely on costly manual construction to generate extreme weather scenarios, an approach largely unchanged since
arXiv:2609.08606v1 Announce Type: cross Abstract: Subseasonal-to-seasonal (S2S) forecasting is societally critical, supporting decision-making in sectors ranging from water and agricultural management
arXiv:2609.03221v1 Announce Type: new Abstract: Counterfactual audits are the standard tool for checking whether a clinical agent treats demographically distinct but clinically identical patients diff
arXiv:2609.03106v1 Announce Type: new Abstract: Scaling laws in modern deep learning describe how held-out loss improves as model capacity, training data, and compute increase, often following power-l