Category: DATA SCIENCE / AI - actuview - the international streaming platform for actuaries

Jornada de Actuarización: Homología persistente aplicada a datos actuariales.

Tue, 15 Sep 2026 09:33:03 +0000

En esta Jornada de Actuarización exploraremos el uso de la Homología Persistente como una herramienta innovadora para el análisis de datos actuariales. Conoceremos sus fundamentos y aplicaciones para identificar estructuras, patrones y relaciones dentro de conjuntos de datos complejos, incorporando nuevas perspectivas al análisis y modelación de riesgos.

Modelos globales para pérdidas de seguros mediante distribuciones matriciales

Tue, 15 Sep 2026 09:26:17 +0000

Proponer un modelo único y flexible para representar todas las pérdidas en seguros (incluyendo extremos), usando distribuciones tipo fase, evitando la separación tradicional y mejorando la coherencia del análisis actuarial.

Clarity Podcast - Episode 5: Obesity Drugs - Game Changers or Overhyped

Tue, 01 Sep 2026 09:34:15 +0000

Are GLP-1 weight loss drugs like Ozempic the miracle cure they are made out to be on social media, or are they simply overhyped? In this episode of the ASSA Clarity podcast, host Lucienne Fild sits down with Desigan Pillay, a member of the Actuarial Society of South Africa, to unpack the rapid rise of metabolic health advancements and what they mean for the insurance and actuarial landscapes. Desigan breaks down the three buckets of metabolic health and shares unique insights into South Africa’s distinct obesity demographics. The discussion dives deep into the realities of long-term usage – touching on drug adherence, "yo-yo" usage, muscle and bone mass loss, and the socioeconomic "haves and have-nots" of healthcare access. Finally, they explore the direct operational impacts for life, health, and disability insurers. Tune in for an insightful, data-driven conversation on whether the actuarial profession is ready for the GLP-1 revolution.

DENK LAUT – der Podcast: FIT4AI, Episode 9: KI Serviceanbieter

Thu, 27 Aug 2026 12:32:16 +0000

In Episode 9 unserer Reihe Fit4AI im DAV-Podcast DENK LAUT wagen wir einen Perspektivwechsel: Erstmals haben wir keine Aktuarinnen oder Aktuare am Mikrofon, sondern blicken ganz bewusst von außen auf die Branche. Gemeinsam mit unseren Gästen Pascal Godejohann (AI Evangelist bei HDI) und Simon Moser (CEO von Muffintech) beleuchten wir die Praxis von KI-Projekten im Versicherungsumfeld. Wir sprechen über typische Stolpersteine beim Übergang vom ersten Proof-of-Concept in den produktiven Betrieb, werfen einen Blick auf den aktuellen Reifegrad von Versicherern und Serviceanbietern und diskutieren, an welchen harten KPIs und Metriken sich KI-Initiativen heute messen lassen müssen. Darüber hinaus stellen wir auch die Frage, ob die anfängliche Hype-Welle allmählich abflacht oder die eigentliche Transformation jetzt erst richtig Fahrt aufnimmt.

Open IRM – A Publicly Accessible Internal Risk Model of an Artificial Life Insurer for Analyzing and Benchmarking Actuarial Methods in the Solvency II Setting

Tue, 25 Aug 2026 15:23:04 +0000

Setting Predicting and understanding solvency figures poses major challenges for life insurers. Internal risk models provide realistic representations, but they remain company-internal and make model understanding difficult. This makes it hard to compare new machine-learning methods for prediction and explainability objectively and under realistic scenarios. We developed openIRM, an open-source internal risk model for an artificial life insurer. It combines an economic scenario generator based on the G2++ interest-rate model with a cash-flow projection model. The model supports both outer real-world simulations and inner simulations for determining basic own funds. It has also been calibrated for all trading days from September 2016 to December 2023 and allows the use of direct and indirect valuation methods, which have been shown to produce consistent results. This talk gives a short introduction to risk modelling for a life insurer under Solvency II, outlines how the openIRM model works, and demonstrates its practical use through a concrete example.

El comportamiento Ético en la profesión actuarial y su relevancia ante los avances tecnológicos, particularmente Inteligencia Artificial

Thu, 20 Aug 2026 07:51:29 +0000

El comportamiento Ético en la Profesión Actuarial y su relevancia ante los avances tecnológicos, particularmente Inteligencia Artificial Acompaña al Act. José Luis Lobera Topete en una reflexión fundamental sobre los principios, fundamentos y la responsabilidad ética del actuario frente a la IA.

Plenary IV: AI in Insurance: Innovation, Responsibility, and Governance

Fri, 31 Jul 2026 14:38:07 +0000

Panelists:

Nivien Shafik, Head of AI Governance & Strategy, Munich Re
Fabrice Staad, General Manager, ALAN
Bogdan Tautan, Reinsurance Analyst and Capital Actuary, Achmea Reinsurance
Cornelius Vogel, Google Cloud Insurance Industry Lead EMEA, Google

Moderated by Christophe Heck, Board Member, Actuarial Association of Europe (AAE)

Introducing AI in Pension Planning: A Comparative Study of Deep Learning and Fuzzy Mamdani Inference Systems for Estimating Replacement Rates

Fri, 31 Jul 2026 13:02:58 +0000

Funded pensions have gained considerable attention as a strategy for securing supplementary income in retirement. This paper presents a comparative analysis of two methods for estimating the replacement rate: a deep learning model and a Fuzzy Mamdani Inference System (FIS). Trained on synthetic datasets, the deep learning model demonstrated high accuracy in predicting replacement rates compared to exact solutions. Meanwhile, the FIS, which relies on expert knowledge and experience, showed promising results but highlighted the need for further refinement of interval and linguistic category definitions. The study underscores the importance of introducing artificial intelligence (AI) techniques, such as neural networks and fuzzy logic, in the realm of pension planning. These tools, though not extensively explored in this context, are crucial for developing decision support systems, particularly in big data scenarios. Such systems can provide preliminary estimates of replacement rates, thereby aiding experts in their subsequent decision-making processes. Multi-criteria decision analysis is also suggested as a future research direction to further enhance decision-making in multi-pillar pension systems.

From Text to Actuarial Modelling: The Role of NLP and LLMs in Cyber Risk Assessment

Fri, 31 Jul 2026 12:56:27 +0000

Cyber risk has become one of the main challenges facing the insurance industry today. For the seventh consecutive year, cyber risk has been ranked as the Number 1 concern for the insurance sector, ahead of climate risk by France Assureurs in the Prospective Mapping Report 2025. As an emerging risk, it is characterized by scarce historical data, high claim heterogeneity, and the occurrence of extreme events with major financial impacts. To address these challenges, this research explores the potential of textual data as a novel source of actuarial information. The Privacy Rights Clearinghouse (PRC) database, which has recorded thousands of data breach incidents since 2005, provides a key foundation for analysis. Prior work by Kher, Lopez and Rapior (2023) demonstrated that textual incident descriptions can be leveraged through Natural Language Processing (NLP) and neural networks to assess claim severity even in the absence of quantitative information. Using the updated PRC 2025 extraction, this study extends the analysis by mobilizing Artificial Intelligence and Large Language Models (LLMs) to structure and exploit unstructured text, thereby improving the actuarial modelling of cyber claims. The main methodological steps include:

A comparative analysis of PRC databases (2019 vs 2025), data harmonization, and the application of Extreme Value Theory to characterize cyber loss severity;
The classification of incidents using machine learning algorithms;
Severity modelling through sequential neural networks (LSTM), which outperform classical models (logistic regression, SVM, random forests, XGBoost), particularly for high-severity claims (starting at the 95th–99th percentiles of the severity distribution);
The generation of synthetic incident descriptions using LLMs to enrich training datasets and simulate extreme scenarios;
The evaluation of model robustness and the contribution of synthetic data to improving predictive effectiveness.

Results confirm the significant contribution of NLP and Deep Learning to cyber risk quantification. While traditional models remain suitable for moderate losses, LSTM architectures prove more effective at identifying and characterizing severe claims. The integration of feature extraction through regular expressions, and the use of generative LLMs further enhance model accuracy and robustness. Beyond its technical dimension, this workshop illustrates that exploiting textual data represents a strategic opportunity for the insurance sector: it enables richer claim databases, faster and more accurate loss assessment upon incident notification, and improved prudential anticipation (Solvency II, ORSA). Ultimately, this approach highlights the complementarity between actuarial expertise and data science, fostering a deeper understanding of cyber risks and strengthening the resilience of the insurance industry in the face of rapidly evolving digital threats.

How AI and New Regulations Are Redefining the European Actuarial Pricing

Fri, 31 Jul 2026 12:36:38 +0000

New technologies, artificial intelligence, and evolving European regulations are reshaping the pricing landscape and transforming how insurers design and govern their pricing frameworks. Far from limiting innovation, regulation can act as a catalyst for modernization driving transparency, harmonization, and stronger governance while enabling the use of advanced analytical techniques. This session explores how actuarial and pricing teams can modernize their workflows and techniques to meet new technological and regulatory demands. Collaboration between new business and renewal teams is now essential. We will discuss how this can be achieved and supported by harmonized and transparent processes that enhance agility, consistency, and compliance. Under growing uncertainty and new market dynamics, actuaries are also challenged to rethink long-standing assumptions, compare alternative scenarios rapidly, and adapt strategies to evolving conditions. Collaboration and operational alignment will be key. Establishing a harmonized and auditable process that enables faster, well-informed and robust decision-making while maintaining a strong governance structure is the new challenge. The session will also highlight strategies to balance sophistication with explainability, stakeholder communication, and operational efficiency. Finally, we will discuss lessons from other markets, contrasting the European experience with the British and other regulated contexts, and drawing insights from the measures already implemented in the UK.Attendees will gain practical guidance on how actuarial functions can modernize their structures, enhance stakeholder communication, and embrace innovation while maintaining rigor and accountability.

Imbalanced Regression: Definition, Impacts and Solutions

Fri, 31 Jul 2026 11:28:21 +0000

Data quality and availability are central challenges in statistical and machine learning, particularly when modeling rare events. This work investigates the use of synthetic data generation as a means to enhance the performance of standard learning approaches in imbalanced settings, covering both classification and regression tasks. Generating realistic synthetic tabular data is inherently difficult, as it requires faithfully capturing complex inter-variable relationships. While generation can be performed directly in the original feature space, we demonstrate that representation learning offers significant advantages by uncovering non-linear correlations and improving the overall quality of the generated samples. We first provide a comprehensive analysis of data imbalance, characterizing its impact both empirically and theoretically. We then propose several data generation strategies designed to mitigate this imbalance and improve model accuracy and generalization. By extending imbalanced data management beyond binary classification to regression and more complex real-world settings where rare outcomes are often the most consequential, this work opens new perspectives for statistical modeling and machine learning in high-stakes applications.

The Role of Actuaries in AI and AI Governance

Fri, 31 Jul 2026 11:21:10 +0000

Artificial intelligence is no longer a future risk for the insurance industry, it is a present operational reality. Yet the governance frameworks designed for classical models are structurally inadequate for AI systems that operate continuously at scale, behave adversarially, and propagate errors across fragmented value chains. This presentation makes three arguments. First, that the risk characteristics of modern AI map more closely onto cybersecurity governance paradigms than onto traditional model risk frameworks. Second, that AI governance responsibility is structurally fragmented, cutting across multiple internal functions and multiple regulatory regimes simultaneously, with no single framework and no single organisational actor currently owning the full picture. Third, that the actuarial profession, trained in model validation, embedded in the regulatory architecture through a signed and auditable opinion, and positioned across the full insurance value chain, is a natural candidate to bridge this gap. The presentation concludes with an honest assessment of where the profession stands today, acknowledging both the genuine strengths actuaries bring to AI governance and the gaps in curricula, risk literacy, and European professional coordination that must be closed. The central argument is not that actuaries are uniquely qualified. It is that they are uniquely positioned, and that the difference is the profession's responsibility to address.

Bias diagnostics for Black-Box Actuarial AI Systems under evolving regulations

Fri, 31 Jul 2026 11:14:18 +0000

The use of artificial intelligence (AI) in actuarial science is becoming increasingly widespread, from risk assessment to claims management, but these models may inadvertently introduce discriminatory biases, either directly through the use of sensitive attributes (e.g. gender, ethnicity) or indirectly through variables correlated with them. Prior research has shown that simply removing sensitive variables is insufficient and may even amplify unfairness. At the same time, regulatory frameworks (e.g. Gender Directive 2012, Colorado 10-1-1 Regulation, NAIC Model AI Bulletin, EU AI Act) are imposing stricter requirements for transparency, fairness and accountability in AI-based decision making. In this work, we map these key regulations relevant to actuarial practice and link them to the two fundamental forms of bias: direct and indirect. We show that this distinction forms the foundation for understanding widely used concepts such as disparate treatment, disparate impact and proxy discrimination. We then propose a structured decomposition of direct and indirect biases and illustrate how they manifest in a life insurance case study. This unified perspective clarifies the regulatory implications of bias in actuarial AI models and offers practical guidance for actuaries seeking to design compliant, ethically sound and robust AI systems.

Implementing and Governing AI: from use cases to best practices in AI adoption

Thu, 30 Jul 2026 10:25:08 +0000

The presentation will provide an overview of the ongoing activities of the International Actuarial Association’s AI Task Force, focusing on how actuaries can implement AI. It will highlight use cases that not only address gaps within the actuarial community but also inspire attendees with practical examples, offering new ideas and application areas where actuaries can contribute.In addition to implementation and use cases, attention will be given to how actuaries can adopt AI, including best practices and what an effective implementation framework might look like. Taking an end-to-end approach to AI adoption, the presenters will explore various governance aspects such as data selection, training, testing, model development, selection, monitoring, and the operation of AI systems.All examples will relate to actuarially relevant topics and demonstrate how actuaries can successfully adopt AI. The information presented will be based on the work of the Task Force, providing materials and insights from the ongoing activities developed at the IAA.

Democratizing Actuarial Expertise Through Fine-Tuned Chain of Thoughts

Wed, 29 Jul 2026 11:34:42 +0000

The actuarial profession faces an unprecedented challenge: while insurance complexity grows exponentially, qualified actuarial expertise remains scarce and concentrated. This research introducesa groundbreaking approach to democratize actuarial reasoning through fine-tuned Large Language Models implementing an innovative ”Actuarial Chain of Thoughts” methodology. Our framework transforms how non-specialists access and utilize advanced actuarial knowledge, enabling natural language interactions with AI systems that replicate expert actuarial reasoning patterns.We present a novel architecture combining domain-specific fine-tuning of advanced reasoning models with automated RMarkdown report generation. The system captures the structuredthinking process of experienced actuaries through specialized training on curated actuarial problem-solving sequences. Users interact through conversational interfaces, posing complex insurance questions in plain language.The training dataset comprises 15,000 carefully curated examples extracted from over 30,000 pages of open-source actuarial science literature, covering all major domains of actuarial practice.The framework’s impact extends beyond efficiency gains, fundamentally reshaping how insurance organizations leverage actuarial insights through transparent, explainable AI reasoning.

A GAN-based climate scenario generator for risk management and insurance: the case of drought

Wed, 29 Jul 2026 09:09:20 +0000

According to the 2025 report by France Assureurs on the cartography of emerging risks, climate-related hazards rank first in France in terms of both frequency and severity. Among these, drought emerges as one of the most critical risks, affecting not only France but also numerous regions worldwide. In fact, drought events have accounted for approximately 30% of the total indemnities paid under the French CatNat regime (Régime d’indemnisation des catastrophes naturelles). This paper introduces an artificial intelligence framework based on Conditional Generative Adversarial Networks (Conditional GANs) designed to generate future spatio-temporal trajectories of climatic indices. The focus is placed on the uniform Soil Wetness Index (SWI), a key indicator employed in France to quantify drought severity. The proposed model, referred to as SwiGAN, is developed to simulate plausible drought propagation patterns over time for a region in France particularly exposed to this hazard. By generating realistic sequences of SWI maps, SwiGAN provides new insights into the dynamics and propagation mechanisms of droughts under climate change scenarios. The resulting trajectories can inform the design of adaptive risk management and insurance strategies, contributing to enhanced resilience to climate extremes. Beyond its application to drought modeling, the proposed methodology offers a generalizable framework that can be extended to other climate-related perils or adapted for actuarial applications, such as economic scenario generation.

Ethical Artificial Intelligence caught between regulation and responsibility

Tue, 28 Jul 2026 12:58:07 +0000

The use of Artificial Intelligence (not only, but especially in insurance) is always associated with ethical issues and requires careful analysis of the risks and dangers (also with regard to reputation). On the other hand, the insurance industry is extensively regulated and falls under the AI Act in particular.

Against this backdrop, the lecture will address the following question: Is it sufficient for us actuaries to comply with the law when using Artificial Intelligence in order to meet ethical standards? Without giving too much away (spoiler alert): No!

After a brief overview of AI regulations in insurance, selected ethical issues will be discussed, compared with regulatory requirements, and gaps that require our special attention will be identified.

Migration des outils, les actuaires au cœur de la stratégie

Tue, 28 Jul 2026 06:25:51 +0000

Licences qui flambent, montées de version imposées, pénurie de compétences… De plus en plus d’acteurs revoient leurs outils de modélisation et de traitement de données.SAS, Matlab, Prophet : ces solutions historiques sont parfois devenues des freins. Alors on parle migration, open source, Python, R… mais aussi gouvernance, qualité des modèles, documentation.Et dans tout ça, l’actuaire n’est plus un simple utilisateur. Il devient un acteur clé de la transformation – et ça change tout. Car ce mouvement technique et stratégique peut aussi redonner de l’attractivité à la profession : le métier d’actuaire attire désormais des profils plus hybrides, plus tech, plus curieux. Pour parler de ce sujet, Grégoire Tournon accueille à la Maison des Actuaires Nicolas Lorin, co-responsable de l’actuariat chez aVB.

Comment les actuaires façonnent des agents (vraiment) intelligents pour l’assurance

Tue, 28 Jul 2026 06:07:06 +0000

Dans ce nouvel épisode des "Voix de l'Actuariat", la collection de podcasts proposée par l'Institut des actuaires, on explore comment les agents intelligents réinventent le quotidien et le rôle des actuaires. Données massives, décisions accélérées, attentes clients transformées... L’assurance entre dans une nouvelle ère, une ère où l’intelligence artificielle ne se contente plus d’assister… Elle décide, apprend, interagit. Elle devient un acteur à part entière. Au cœur de cette montée en puissance, un concept s’impose : l’agent intelligent. Capable de gérer un sinistre, dialoguer avec un client, optimiser un processus en temps réel…Ces agents changent la donne, et les actuaires doivent, eux aussi, se réinventer. Concepteurs, superviseurs, stratèges ? Quel est leur rôle face à cette transformation ? Pour répondre à ces interrogations, Grégoire Tournon échange aujourd’hui à la Maison des Actuaires avec Swan Broutard, consultante en actuariat du cabinet Exiom Partners.

Advanced Applications of Generative AI in Actuarial Science: Case Studies Beyond ChatGPT

Mon, 27 Jul 2026 15:02:02 +0000

In this presentation, we will explore the transformative impact of Generative AI (GenAI) on actuarial science, illustrated by case studies including a live demo. GenAI offers significant opportunities to enhance, innovate, and augment traditional actuarial work. We begin with a brief historical overview of AI, followed by four practical case studies that demonstrate how Large Language Models (LLMs) can enhance actuarial processes. For two of the case studies, we will also present a live demo showcasing the functionality and results.First, we show how LLMs improve claims cost prediction by engineering features from unstructured textual data. Second, we investigate the automation of market comparisons using Retrieval-Augmented Generation, a GenAI concept designed to identify and process relevant information from documents. The third case study highlights the capabilities of fine-tuned vision-enabled LLMs in classifying car damage types and extracting contextual information. Lastly, we present a multi-agent system that autonomously analyzes datasets to generate comprehensive reports detailing key findings. We conclude by outlining further potential applications of GenAI within the insurance industry and discussing ethical and technical challenges and considerations associated with its use.

KEYNOTE: Recent Developments in AI and the IAA AI Task Force Update

Mon, 06 Jul 2026 08:35:11 +0000

Development in AI continue to accelerate, some of which could impact the insurance industry in material ways which haven't emerged. This first part of this talk will cover recent relevant developments in AI, such as computer use, learning, the boundaries of intellectual property as well as the potential impact for the insurance industry. If you don't see the connection, then you should attend this talk. The second half of this talk will cover the work of the IAA's AI Task Force to help our profession produce AI-enabled actuaries, as well as how folks can get up-to-speed quickly.

The Stuff of Models: Intellectual Property & Copyright Concerns

Mon, 06 Jul 2026 08:27:35 +0000

Many AI models rely on data and information that is "scraped" from open sources on the Internet. The assembly and use of this vital "stuff of models" is often done without the knowledge and consent of the owners of the data. Is this theft or merely fair use of intellectual property? And yet, on reflection, who can ever really own an idea?Although the EU has regulations governing data use, many of the largest IT companies are based in the US — and that is where legal battles are currently being waged. Some people assert that denying free and unfettered access to data will necessarily inhibit innovation, while others say just the opposite. It is even suggested that piracy has been one of the historical drivers of capitalism.Actuaries traditionally provide reliance statements when their work product includes information provided by others. How important is it that an actuary i) knows exactly what data has been "captured" to fuel a third party AI tool that they have used, and ii) discloses the use of such a tool to clients and regulators? What happens if an error is found in one's actuarial work product, and that error is subsequently traced back to the use of a generative AI tool? The ethical provenance and responsible use of data is a subject of importance to actuaries as well as those who rely on their work.

Harnessing Generative Models for Synthetic Non-Life Insurance Data

Mon, 06 Jul 2026 08:23:13 +0000

Obtaining realistic, publicly accessible datasets is a significant barrier in advancing actuarial research and developing open-source tools for insurance analytics. This study leverages synthetic data and aims to evaluate various Generative Models for producing a standard synthetic non-life insurance premium dataset. A Conditional Gaussian Mixture Model has been employed as a benchmark. The methodology involved splitting the dataset into two subsets based on the "claim occurence" variable as a binary indicator. For each subgroup, a multivariate Gaussian Mixture Model is fitted, allowing for complex, multi-modal distributions. This benchmark was then compared with advanced Deep Learning architectures, including a Conditional Variational Autoencoder, a Conditional Variational Autoencoder with a Transformer-based Decoder, and a Conditional Diffusion Model. Additionally, the GPT-5.1 Large Language Model was used to generate synthetic datasets via prompt. The experiments were conducted on two insurance datasets retrieved from the CASdatasets R package following three trials. In the first experiment, used as a baseline quality assessment, a portion of each complete dataset was fed into the generative models, which were tasked with generating an equal number of records. In the second experiment, used to evaluate the data augmentation capacity, a smaller portion of each dataset was used, with the models tasked with producing a larger number of rows, equal to that in the first experiment. In the final trial, with attention to ethical considerations, the gender variable was omitted to protect privacy. Validation of the generated data included several steps: data visualization with comparison through univariate analysis, PCA, and UMAP representations, evaluation of the consistency of the produced data with the original, and the statistical Kolmogorov-Smirnov test. Predictive modeling of frequency and severity using Generalized Linear Models (GLMs) based on Tweedie distribution were employed to assess the quality of the generated data. Furthermore, the importance of features was analyzed. This analysis evaluates each model’s ability to accurately capture underlying distributions, preserve complex dependencies, and maintain intrinsic relationships. The findings offer valuable insights for improving synthetic data generation in the insurance field, potentially enhancing risk modeling, pricing strategies in the face of data scarcity, and ensuring regulatory compliance.Keywords:Conditional Variational Autoencoder, Conditional Gaussian Mixture Model, Conditional Diffusion Model, Conditional Variational Autoencoder with a Transformer-based Decoder, GPT-5.1 Large Language Model, PCA, UMAP, GLMsReferences:1. Ian Goodfellow and Yoshua Bengio and Aaron Courville, 2016, Deep Learning, MIT Press.2. Mario V. Wuthrich, Ronald Richman, Benjamin Avanzi, Mathias Lindholm, Michael Mayer, Jürg Schelldorfer, Salvatore Scognamiglio, 2025, AI Tools for Actuaries, SSRN.3. David Foster, 2023, Generative Deep Learning, 2nd Edition, O'Reilly.4. Jake VanderPlas, 2016, Python Data Science Handbook, O'Reilly.5. Jamotton, Charlotte ; Hainaut, Donatien, 2023, Variational autoencoder for synthetic insurance data, ISBA.6. Harshvardhan GM, Mahendra Kumar Gourisaria, Manjusha Pandey, Siddharth Swarup Rautaray, 2020, A comprehensive survey and analysis of generative models in machine learning, ScienceDirect.

The State of GenAI Integration Progress at European Insurers

Mon, 06 Jul 2026 08:17:31 +0000

An overview of the results of our survey of European Insurers on their GenAI implementation journeys. This will include a status update on where the market generally is, what the leaders are doing and the key challenges faced.

AI Use Cases in Insurance Pricing

Mon, 06 Jul 2026 08:14:21 +0000

The insurance pricing landscape is at a pivotal crossroads, where the integration of AI agents can truly become a competitive advantage. This talk provides a comprehensive exploration of how agentic AI can reshape the pricing ecosystem to drive precision and operational excellence.The presentation will open with a brief overview of an end-to-end pricing process, establishing a foundational understanding of the traditional workflow. Building on this context, we will conduct a detailed review of high-impact AI use cases specifically curated to improve efficiency across the value chain - from data processing and risk differentiation to the acceleration of rate deployment and automated portfolio monitoring.The talk concludes with an actionable list of items pricing leaders can introduce to improve their pricing landscape and successfully welcome AI. We will examine how pricing leaders can prepare for a world of autonomous agents, balancing the immense opportunities for innovation with the critical need to mitigate systemic risk and ensure both regulatory and ethical compliance.Join to discover how to harmonize cutting-edge technology with rigorous pricing framework to future-proof your organization.