Actuarial science, traditionally rooted in statistical modeling and financial mathematics, serves as the backbone of risk management in insurance, pensions, and financial planning. With the exponential growth of data and computational power, the field faces increasingly complex challenges: assessing extreme events, managing uncertainties, and providing transparent, explainable predictions to regulators and stakeholders. In recent years, artificial intelligence (AI), particularly generative AI models, has emerged as a transformative force capable of reshaping the actuarial landscape.
While ChatGPT and similar large language models (LLMs) have demonstrated remarkable capabilities in natural language generation, reasoning, and preliminary predictive tasks, their applications in actuarial science are limited. These models excel in producing coherent textual outputs and summarizing complex topics, but they often lack domain specificity, rigorous numerical accuracy, and the transparency required for regulatory compliance. Consequently, the actuarial profession has begun exploring advanced generative AI architectures that extend beyond conventional LLMs to provide specialized analytical capabilities tailored to complex financial and risk datasets.
This article presents a comprehensive case study on the advanced application of generative AI in actuarial science, emphasizing scenarios where domain-specific adaptation, multi-modal data integration, and interpretable modeling are critical. By examining insurance pricing, reinsurance optimization, pension planning, and regulatory reporting, we demonstrate how generative AI can enhance predictive accuracy, decision support, and scenario analysis, while addressing inherent limitations of general-purpose language models.
The study aims to bridge the gap between cutting-edge AI research and practical actuarial implementation, offering insights for both practitioners and the broader public. By showcasing concrete applications and identifying both technical and ethical considerations, this work underscores the transformative potential of AI-driven actuarial science and highlights a pathway toward more intelligent, transparent, and reliable financial decision-making. Through this lens, we aim to demonstrate that the integration of generative AI not only augments human expertise but also establishes a new paradigm in risk modeling and financial analytics.
I. Literature Review and Theoretical Framework
Actuarial science has historically relied on statistical modeling, probability theory, and financial mathematics to quantify and manage risk. Techniques such as generalized linear models (GLMs), survival analysis, and stochastic processes have been central to pricing insurance products, evaluating reserves, and projecting pension liabilities. For decades, actuaries have leveraged historical data to predict future claims, mortality rates, and financial outcomes. These approaches emphasize accuracy, interpretability, and regulatory compliance, which are essential for public trust in financial institutions.
However, the complexity of modern financial systems, the availability of vast heterogeneous data, and the increasing frequency of extreme events pose challenges that traditional models alone cannot fully address. For example, catastrophic risks like pandemics or climate-driven disasters exhibit non-linear dependencies that classical actuarial models struggle to capture. Moreover, scenario analysis and stress testing demand rapid adaptation to dynamic environments, which often exceeds the capabilities of purely statistical models.
Artificial intelligence has recently begun to complement traditional actuarial methods by offering advanced predictive capabilities and automation. Machine learning (ML) models, such as decision trees, random forests, gradient boosting, and neural networks, have been applied to claims prediction, fraud detection, and customer segmentation. These models excel at capturing complex, non-linear patterns in large datasets, enabling actuaries to identify hidden risk factors that may elude conventional statistical methods.
Generative AI represents the next evolution in this trajectory. Unlike traditional ML models, which primarily focus on prediction or classification, generative AI models can synthesize new data, simulate scenarios, and generate human-readable outputs. This ability to “create” plausible scenarios rather than merely analyze existing data is particularly valuable for actuarial applications such as stress testing, scenario planning, and policy optimization.
Large language models such as ChatGPT have demonstrated impressive natural language understanding, reasoning, and text generation abilities. In actuarial contexts, these models can assist with summarizing reports, generating explanatory notes, or even drafting preliminary analyses of risk scenarios. However, several limitations constrain their practical adoption in professional actuarial tasks:
Domain specificity: ChatGPT is trained on general-purpose text data and lacks deep expertise in actuarial mathematics or regulatory frameworks.
Numerical precision: While capable of handling qualitative reasoning, ChatGPT may produce approximations or errors in quantitative calculations.
Explainability and compliance: Regulatory standards require transparency in decision-making processes, which is difficult to guarantee with general LLM outputs.
Consequently, while ChatGPT can support communication and documentation, its ability to drive core actuarial decision-making remains limited.
To overcome these limitations, researchers and practitioners are exploring domain-specific generative AI models. These models integrate actuarial theory with advanced AI architectures, often combining multiple modalities (numerical, textual, and temporal data). Key characteristics include:
Domain adaptation: Fine-tuning models on actuarial datasets, including claims history, financial reports, and regulatory documents, improves relevance and precision.
Scenario generation: Generative models can simulate rare events, stress conditions, and extreme market movements, facilitating better risk management.
Interpretability: Techniques such as attention visualization, rule-based augmentation, and probabilistic modeling ensure outputs are explainable to stakeholders.
Integration with traditional methods: Hybrid approaches combine stochastic actuarial models with generative AI, leveraging the strengths of both paradigms.
This study builds on a theoretical framework that positions generative AI as a complementary tool to traditional actuarial models, rather than a replacement. The framework emphasizes:
Predictive augmentation: AI models enhance the accuracy of forecasts for insurance claims, investment risks, and longevity.
Scenario exploration: Generative AI simulates multiple plausible futures, capturing uncertainty beyond historical data patterns.
Decision support and transparency: AI outputs are structured to be interpretable and auditable, aligning with professional standards.
Human-AI collaboration: Actuaries remain central in validating models, interpreting results, and making strategic decisions, ensuring responsible application.
By situating generative AI within this framework, actuarial science can leverage computational innovation while maintaining the rigor, transparency, and ethical standards that define the profession.
Existing literature shows promising applications of AI in actuarial tasks, yet several gaps remain:
Most studies focus on predictive tasks rather than generative scenario modeling.
Few models address explainability in a regulatory context.
Integration of multi-modal data, such as economic, climate, and demographic information, is still limited.
Comprehensive case studies demonstrating AI applications beyond ChatGPT in actuarial science are scarce.
This gap motivates the present study, which explores advanced generative AI applications across multiple actuarial scenarios, illustrating both practical benefits and theoretical implications.
II. Research Methodology
The primary objective of this study is to investigate the advanced applications of generative artificial intelligence (AI) in actuarial science, focusing on tasks that extend beyond the capabilities of general-purpose language models like ChatGPT. Specifically, the study aims to:
Evaluate generative AI’s ability to enhance actuarial modeling, scenario generation, and decision support.
Compare the performance of specialized AI models with ChatGPT in actuarial contexts.
Assess the interpretability, reliability, and regulatory compliance of AI-generated outputs.
To achieve these objectives, a case study approach was employed. This approach allows for in-depth exploration of real-world actuarial tasks, capturing both quantitative and qualitative insights. Case studies were selected across diverse actuarial domains, including insurance pricing, reinsurance optimization, pension planning, and regulatory reporting. Each case was designed to test AI capabilities in handling complex, multi-dimensional, and uncertain datasets, reflecting the challenges faced by modern actuaries.
Data integrity and representativeness are crucial in actuarial modeling. For this study, multiple data sources were utilized:
Historical Insurance Claims Data: Large-scale datasets from public and proprietary insurance repositories, covering life, health, and property insurance claims over the past decade. Variables include policyholder demographics, claim amounts, policy types, and temporal claim patterns.
Financial Market and Macroeconomic Data: Time series data for interest rates, inflation, equity returns, and credit spreads to model investment and pension-related risks.
Synthetic Data: To simulate rare events such as natural disasters, pandemics, or extreme market shocks, synthetic datasets were generated using stochastic models and historical extremal distributions.
Regulatory and Compliance Documents: Publicly available actuarial guidelines, Solvency II reports, and risk management frameworks were included to test AI’s ability to produce compliant documentation.
Data preprocessing involved cleaning missing values, standardizing numerical variables, encoding categorical data, and ensuring temporal alignment across datasets. Special attention was given to data normalization and feature engineering to capture non-linear interactions between risk factors.
The study compares multiple AI architectures to evaluate their actuarial capabilities:
ChatGPT (Baseline): Used as a benchmark for natural language generation and preliminary risk scenario analysis. While not specialized for actuarial tasks, it serves as a reference point for comparison.
Domain-Specific Generative AI Models:
Fine-tuned Large Language Models (LLMs): Pretrained transformer models adapted to actuarial datasets, including claims history, financial reports, and regulatory guidelines.
Hybrid Stochastic-Generative Models: Integrate traditional actuarial stochastic simulations with generative AI for scenario expansion, rare-event prediction, and stress testing.
Multi-Modal Generative Models: Capable of handling textual, numerical, and temporal data simultaneously, providing richer, context-aware predictions.
These models were trained and validated on a combination of historical and synthetic datasets. Fine-tuning involved iterative optimization to improve numerical accuracy, scenario fidelity, and textual coherence.
To rigorously assess model performance, multiple evaluation dimensions were considered:
Predictive Accuracy: Mean squared error (MSE), root mean squared error (RMSE), and log-likelihood metrics were used for numerical predictions, such as claims frequency and severity.
Scenario Realism: Assessed qualitatively by actuarial experts, evaluating whether generated scenarios reflected plausible risk dynamics.
Interpretability and Transparency: Measured through attention visualization, rule-based alignment, and compliance checks against regulatory standards.
Computational Efficiency: Training and inference time, as well as resource consumption, were recorded to assess scalability for industry deployment.
Human-AI Collaboration Potential: Evaluated by comparing AI-generated outputs with actuaries’ expert judgment in terms of actionable insights and decision support.
The study employed four representative actuarial case studies:
Insurance Product Pricing: Models were tasked with generating risk-adjusted pricing for life and property insurance products, considering multiple risk factors including demographics, claims history, and macroeconomic conditions. The generative AI models were evaluated on their ability to produce both accurate and interpretable pricing outputs.
Reinsurance and Capital Optimization: AI models simulated catastrophic loss scenarios to optimize reinsurance strategies. Scenarios included low-probability, high-impact events, emphasizing the model’s ability to handle tail risks and extreme market conditions.
Pension and Retirement Planning: Generative AI was applied to project long-term pension liabilities under multiple economic and demographic assumptions. Models were assessed on their ability to generate realistic, long-term forecasts, including stress testing for longevity and market volatility.
Regulatory Reporting and Documentation: AI models generated draft actuarial reports and compliance documents. Evaluation focused on coherence, alignment with regulatory requirements, and the potential to reduce manual workload for actuaries.
Each case study involved iterative validation by professional actuaries to ensure practical relevance and reliability of outputs. Feedback loops allowed model refinement, enhancing scenario realism and interpretability.
This research methodology integrates traditional actuarial rigor with advanced AI capabilities, enabling a comprehensive assessment of generative AI beyond general-purpose models like ChatGPT. By combining diverse data sources, specialized model architectures, multi-dimensional evaluation metrics, and domain-expert validation, the study ensures both scientific robustness and practical applicability. This approach provides a clear framework for understanding how generative AI can transform actuarial practice, offering quantitative precision, scenario versatility, and enhanced decision support while maintaining transparency and regulatory compliance.
Insurance pricing is a core actuarial task, requiring accurate estimation of risk-adjusted premiums based on historical claims, demographics, policy features, and macroeconomic factors. Traditional models, such as generalized linear models (GLMs), provide interpretable outputs but can struggle with non-linear dependencies and complex interactions.
In this study, generative AI was applied to automate and enhance pricing models. Fine-tuned large language models (LLMs) were trained on extensive insurance datasets, learning patterns from claims histories, policyholder profiles, and financial market data. The models were tasked with generating pricing recommendations for life and property insurance products.
Key Findings:
Scenario Generation: Unlike traditional models that produce single-point estimates, the AI generated multiple plausible pricing scenarios under varying assumptions of claims frequency and severity.
Personalization: Generative models adapted to individual risk factors, producing more granular and tailored pricing suggestions.
Interpretability: Hybrid models combined stochastic actuarial simulations with AI-generated explanations, allowing actuaries to understand why certain premiums were recommended.
This approach demonstrated that generative AI could improve both the precision and transparency of pricing, supporting more informed decision-making and potentially increasing competitive advantage for insurers.
Reinsurance optimization involves allocating capital to cover extreme losses while minimizing costs. Traditional actuarial models rely heavily on stochastic simulations and extreme value theory, but scenario coverage is often limited by computational constraints.
Generative AI models were applied to simulate catastrophic events such as natural disasters, financial crises, and pandemics. Multi-modal generative models incorporated numerical, temporal, and textual data to create realistic loss distributions and tail-event scenarios.
Key Findings:
Enhanced Tail Risk Modeling: AI-generated scenarios captured rare, high-impact events more comprehensively than traditional simulations, helping insurers anticipate extreme losses.
Capital Allocation Insights: By integrating scenario outputs into optimization models, insurers could better allocate reinsurance budgets and determine retention levels, balancing risk and cost.
Dynamic Adaptation: Generative models allowed rapid updates to risk scenarios in response to new data, supporting real-time decision-making.
These results illustrate that AI-enhanced scenario generation can provide more robust insights for reinsurance strategies, improving financial resilience under uncertainty.
Long-term pension planning requires predicting liabilities over decades, accounting for uncertainties in longevity, market returns, and demographic changes. Traditional actuarial projections can be limited by static assumptions and linear extrapolations.
Generative AI models were applied to simulate long-term pension liabilities under diverse economic and demographic conditions. Multi-scenario generation included varying interest rates, inflation, mortality trends, and workforce dynamics.
Key Findings:
Scenario Flexibility: AI models produced hundreds of plausible futures, allowing actuaries to test sensitivity to extreme market movements and demographic shifts.
Risk Assessment: Probabilistic outputs helped identify potential underfunding risks and guide funding strategies for pension plans.
Stakeholder Communication: Generated visualizations and textual summaries improved communication with trustees and regulators, facilitating informed policy decisions.
This demonstrates that generative AI can extend beyond prediction to strategic planning, enabling pension managers to explore risks and opportunities in a comprehensive, transparent manner.
Compliance with regulatory standards such as Solvency II or IFRS 17 is a critical function for actuaries, requiring accurate reporting, documentation, and scenario disclosure. Manual preparation is time-consuming and prone to errors.
Generative AI models were deployed to produce draft actuarial reports, including risk summaries, scenario analyses, and explanatory notes. Domain-specific fine-tuning ensured alignment with regulatory language and requirements.
Key Findings:
Efficiency Gains: Automated report generation reduced preparation time significantly while maintaining high consistency.
Accuracy and Alignment: AI-generated outputs were cross-validated against traditional actuarial calculations, showing strong alignment in numerical results and interpretative text.
Transparency: Hybrid AI-human workflows allowed actuaries to validate outputs, ensuring regulatory compliance and audit readiness.
This case demonstrates the potential for generative AI to augment administrative and compliance functions, freeing actuaries to focus on higher-value analytical tasks.
Across all four applications, several patterns emerged:
Generative AI Complements Human Expertise: While AI excels at simulating scenarios and producing coherent outputs, human oversight remains essential for interpretation, ethical considerations, and strategic decision-making.
Scenario Diversity Enhances Risk Management: Multi-scenario outputs provide richer insights than single-point predictions, enabling more robust planning and stress testing.
Domain-Specific Fine-Tuning is Critical: Models trained on actuarial and financial data outperformed general-purpose LLMs like ChatGPT in accuracy, relevance, and regulatory alignment.
Transparency and Interpretability: Hybrid approaches combining AI with traditional actuarial methods enhance trust, making outputs actionable for regulators and stakeholders.
The application of generative AI in actuarial science offers tangible benefits:
Enhanced Predictive Precision: AI improves forecasts for claims, liabilities, and capital requirements.
Operational Efficiency: Automation of repetitive reporting tasks allows actuaries to allocate effort to complex analyses.
Strategic Risk Management: Scenario generation supports more comprehensive planning and stress testing.
Public and Stakeholder Communication: AI-generated summaries and visualizations improve transparency and understanding.
These case studies collectively demonstrate that generative AI can transform actuarial practice by:
Extending the analytical capacity of actuaries beyond traditional models.
Generating high-quality, interpretable scenarios for decision support.
Reducing operational workload while maintaining regulatory compliance.
Supporting strategic planning in insurance, pensions, and reinsurance.
In conclusion, the practical applications explored here illustrate the potential for generative AI to go beyond ChatGPT, providing specialized, reliable, and actionable insights in actuarial science. The results also set the stage for further comparative analysis and future development of domain-specific AI solutions in finance and risk management.
The case studies highlight several key advantages of generative AI over traditional methods and general-purpose models like ChatGPT:
Enhanced Predictive Accuracy:
Domain-specific generative AI models capture complex, non-linear relationships in historical data, financial markets, and demographic trends. For instance, in insurance pricing, AI-generated scenarios reflect nuanced interactions between policyholder characteristics, claims history, and macroeconomic conditions, improving risk-adjusted premium estimations.
Scenario Generation and Risk Exploration:
Unlike traditional stochastic models, which often rely on limited Monte Carlo simulations, generative AI can synthesize hundreds of plausible future scenarios. This capacity is particularly valuable for extreme events such as pandemics, natural disasters, or financial crises, providing actuaries with richer insights for stress testing and strategic planning.
Operational Efficiency and Automation:
By automating repetitive tasks such as report generation and preliminary risk assessment, AI frees actuaries to focus on higher-value analytical and strategic decisions. This efficiency reduces turnaround times and mitigates human errors in documentation and compliance processes.
Interpretability through Hybrid Approaches:
Generative AI combined with traditional actuarial models produces interpretable outputs. For example, stochastic simulations can provide the quantitative foundation, while AI-generated text explains scenarios in human-readable form. This hybrid approach enhances transparency for regulators, stakeholders, and non-technical decision-makers.
Personalization and Granularity:
AI models can tailor predictions and recommendations to specific policyholder segments or investment portfolios, enabling personalized risk assessment and pricing strategies that would be challenging using conventional actuarial methods.
Despite its potential, several limitations remain in the deployment of generative AI in actuarial contexts:
Data Dependence:
Generative AI requires high-quality, comprehensive datasets for training. Incomplete or biased data can lead to inaccurate predictions and unrealistic scenarios, especially when modeling rare or extreme events.
Computational Complexity:
Advanced multi-modal generative models require significant computational resources for training and inference, potentially limiting scalability for smaller organizations.
Domain Knowledge Integration:
While AI can process large datasets, it may still lack deep understanding of actuarial principles, legal requirements, and ethical considerations. Purely AI-driven outputs without human oversight may result in misaligned recommendations.
Regulatory and Compliance Constraints:
Regulatory standards demand transparency, auditability, and justification of assumptions. Generative models can produce outputs that are difficult to fully trace, raising challenges for compliance and accountability.
The integration of generative AI introduces several potential risks:
Model Hallucination:
AI may generate plausible but incorrect or inconsistent data, which, if unchecked, could compromise decision-making in insurance pricing, pension funding, or reinsurance strategies.
Overreliance on AI:
Excessive dependence on AI outputs may reduce human critical oversight, leading to potential errors in risk assessment or strategic planning.
Ethical and Privacy Concerns:
AI models trained on sensitive personal or financial data must comply with privacy regulations such as GDPR. Misuse or leakage of data could expose organizations to legal and reputational risks.
Bias Propagation:
AI trained on historical data may inadvertently replicate systemic biases in insurance underwriting or pension allocation, requiring careful mitigation strategies.
Maximizing the benefits of generative AI in actuarial science requires an effective human-AI collaboration framework:
Augmentation, Not Replacement:
AI serves as a tool to augment actuarial expertise rather than replace it. Actuaries provide validation, interpret outputs, and ensure regulatory compliance.
Iterative Feedback Loops:
Continuous feedback from actuaries helps refine model predictions, correct anomalies, and incorporate domain-specific knowledge.
Hybrid Modeling:
Combining traditional stochastic simulations with generative AI enhances both numerical accuracy and interpretability. AI handles scenario expansion and pattern recognition, while human experts provide critical evaluation and contextual understanding.
Transparency and Explainability:
Generative AI outputs should be accompanied by explanatory notes, attention visualizations, and scenario assumptions, ensuring outputs are auditable and comprehensible to regulators and stakeholders.
Strategic Decision Support:
Human-AI collaboration enables more informed strategic planning, from insurance product design to pension fund allocation, balancing computational efficiency with professional judgment.
In summary, generative AI offers transformative potential for actuarial science by:
Enhancing predictive accuracy and scenario exploration.
Automating repetitive tasks while maintaining transparency.
Enabling personalized and context-aware risk assessment.
However, challenges such as data dependence, computational requirements, regulatory constraints, and ethical considerations must be carefully managed. The future of actuarial practice lies in human-AI collaboration, where generative AI augments professional expertise rather than replacing it. This hybrid model ensures reliability, interpretability, and regulatory compliance while unlocking new opportunities for innovation in risk management, insurance, and financial planning.
A key future direction is the creation of actuarial-focused generative AI models. While general-purpose models like ChatGPT offer linguistic and reasoning capabilities, they lack the deep domain knowledge necessary for precise actuarial analysis. Fine-tuning large language models on actuarial datasets—including historical claims, financial reports, pension liabilities, and regulatory frameworks—can improve prediction accuracy, scenario realism, and compliance alignment.
Future models should also integrate multi-modal capabilities, enabling simultaneous analysis of numerical, textual, and temporal data. For example, combining policyholder demographics, market trends, and macroeconomic indicators allows AI to simulate complex risk scenarios more accurately. Developing such specialized models will require collaboration between AI researchers, actuaries, and regulatory experts to ensure outputs are both technically robust and professionally valid.
Modern actuarial tasks increasingly require integrating diverse data sources. Beyond traditional insurance and financial datasets, emerging sources include climate data, social and demographic statistics, and healthcare information. Generative AI models capable of fusing these heterogeneous data types can:
Capture emerging risk patterns, such as climate-driven claims or pandemic-related healthcare costs.
Support long-term strategic planning by simulating complex interactions between economic, social, and environmental factors.
Enable personalized actuarial services, offering tailored recommendations for specific policyholders or pension participants.
Multi-scale modeling—combining micro-level data (individual claims) with macro-level trends (economic indicators, regulatory shifts)—will enhance the relevance and applicability of AI-generated insights in real-world actuarial decision-making.
Transparency and accountability remain critical in actuarial applications. Future generative AI systems must incorporate explainability mechanisms, such as:
Attention visualization: Showing which inputs most influence predictions.
Scenario reasoning: Documenting assumptions, data sources, and calculation logic.
Hybrid modeling: Combining traditional stochastic simulations with AI-generated scenarios to ensure interpretability and traceability.
These features will support regulatory compliance under frameworks like Solvency II, IFRS 17, and local insurance regulations, reducing the risk of audit issues and increasing stakeholder trust. Actuaries will continue to serve as essential validators of AI outputs, ensuring outputs align with professional standards and ethical considerations.
Future actuarial practice is likely to be dominated by human-AI collaboration, where AI systems act as intelligent assistants rather than autonomous decision-makers. In this model:
AI handles repetitive or computationally intensive tasks, including scenario generation, stress testing, and preliminary reporting.
Actuaries provide domain knowledge, strategic judgment, and ethical oversight.
Feedback loops allow continuous refinement of AI models based on expert input, ensuring alignment with both professional standards and evolving risk environments.
This collaborative approach will enhance decision quality, operational efficiency, and strategic agility while mitigating risks associated with overreliance on AI.
As AI assumes a larger role in actuarial work, ethical considerations will become increasingly important:
Bias mitigation: Models must be trained and evaluated to avoid reinforcing historical biases in insurance pricing or pension allocation.
Data privacy: Handling sensitive policyholder or financial data requires robust compliance with privacy regulations.
Fairness and transparency: AI outputs should be interpretable to stakeholders, including policyholders, regulators, and the public.
Integrating ethical safeguards into AI development and deployment will be essential to maintain public trust and professional integrity.
Future research may explore AI-driven innovation in adjacent areas, such as:
Dynamic product design: AI could simulate novel insurance products or pension schemes tailored to evolving risk patterns.
Real-time risk monitoring: Continuous AI analysis of live market and claims data could enable rapid responses to emerging threats.
Financial advisory and planning: Generative AI could support personalized investment and retirement recommendations, balancing risk and return.
Cross-sector collaboration: Integrating AI models with healthcare, environmental, and economic data sources can enhance holistic risk assessment.
These applications expand the role of actuaries from traditional risk management to strategic financial planning and innovation.
Generative AI systems in actuarial science will require continuous learning mechanisms:
Models must be periodically updated with new claims, market conditions, regulatory changes, and emerging risks.
Adaptive algorithms can detect shifts in data patterns, improving resilience against unexpected events.
Continuous monitoring ensures that AI outputs remain reliable, accurate, and aligned with professional standards.
This evolution will create a dynamic feedback loop between data, AI, and human expertise, fostering a robust, adaptive, and forward-looking actuarial practice.
The future of actuarial science will be shaped by the integration of generative AI that is:
Domain-specific and capable of handling complex, multi-modal datasets.
Transparent and interpretable, supporting regulatory compliance and ethical practice.
Collaborative, enhancing rather than replacing human expertise.
Innovative, expanding the scope of actuarial work beyond traditional risk assessment to strategic financial planning and product design.
By embracing these developments, the actuarial profession can leverage AI to address increasingly complex risk landscapes, enhance operational efficiency, and deliver more informed, ethical, and impactful financial decisions.
Generative artificial intelligence represents a transformative force in actuarial science, extending capabilities beyond traditional models and general-purpose language systems like ChatGPT. This study demonstrates that domain-specific AI can enhance predictive accuracy, generate rich and realistic risk scenarios, support regulatory compliance, and automate repetitive reporting tasks, thereby allowing actuaries to focus on strategic and high-value decision-making. Through case studies in insurance pricing, reinsurance optimization, pension planning, and compliance reporting, it becomes evident that generative AI not only improves operational efficiency but also enables more robust, data-driven risk management.
Despite these advantages, challenges remain, including data quality requirements, computational complexity, potential model biases, and the necessity for human oversight. Ethical considerations, transparency, and interpretability are paramount to ensure trust and regulatory alignment. The future of actuarial practice lies in human-AI collaboration, where actuaries guide AI outputs, refine assumptions, and provide professional judgment, creating a synergy that leverages both computational power and domain expertise.
Looking forward, the actuarial profession can benefit from continuous development of domain-specific, multi-modal generative AI models, capable of integrating diverse datasets and simulating complex, long-term risk scenarios. By combining innovation, transparency, and human oversight, generative AI promises to redefine actuarial science, transforming it into a more adaptive, efficient, and strategic discipline capable of addressing the increasingly complex risks of the 21st century.
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