2025 AIRC Seminar

AI-Driven Actuarial Data Processing and Reporting Automation

At the Taiwan Actuarial Institute (AIRC) Seminar, we jointly shared practical experience and observations on AI-driven actuarial data processing and reporting automation with FIS and Andy Leung, an actuarial consultant in Korea. The discussion focused on the structural challenges faced by insurers operating under high-intensity regulatory regimes such as IFRS 17 and ICS/K-ICS, and explored how process automation and AI technologies can be leveraged to significantly enhance operational efficiency, data quality, and governance.

Structural Bottlenecks in Traditional Actuarial Operations

The seminar began by reviewing common pain points across actuarial and reporting processes at many insurers. Traditional actuarial work is highly manual and depends on data from diverse sources—policy administration, claims, finance, and regulatory systems—often with inconsistent formats and uneven quality. As a result, actuaries spend a disproportionate amount of time on data cleansing and validation, leaving limited capacity for higher-value risk analysis and model judgment.

From a reporting perspective, end-to-end processes—from data collection and calculation to aggregation and review—are lengthy and heavily reliant on manual intervention. This not only extends production timelines but also materially increases operational risk. As regulatory requirements tighten and submission timelines compress, traditional operating models struggle to cope with the demands of monthly, quarterly, and regulatory reporting.

Actuarial models themselves also face performance and governance challenges, including long runtimes, inadequate version control, limited traceability of changes, and high sensitivity to data quality. These issues elevate risks related to model validation and auditability.

Dual Challenges of Asset–Liability Integration and Timeliness

In asset–liability management (ALM) and capital measurement contexts, system fragmentation and data discontinuity become even more pronounced. Asset and liability models often operate in silos, lacking an integrated analytical framework, which hinders timely assessment of risk interactions. At the same time, regulators and management increasingly demand near-real-time insights, while traditional batch processing and system performance constraints remain significant bottlenecks.

Cross-functional collaboration further complicates the landscape. ALM and capital analyses typically involve actuarial, investment, finance, and IT teams, each with different capabilities and requirements, making process standardization and result interpretation more challenging.

An AI-Driven End-to-End Automation Architecture

To address these challenges, the seminar presented an AI-centric, end-to-end automation architecture encompassing the following key components:

• Data Ingestion and Integration: Automated extraction of data from core systems, external databases, and Excel files to establish a consistent and fully traceable actuarial data foundation.
• AI-Based Intelligent Cleansing and Validation: Machine learning techniques to automatically detect anomalies, fill data gaps, and perform real-time data quality checks.
• High-Performance Actuarial Computation: The use of distributed and parallel computing to significantly shorten model runtimes, supported by elastic cloud resources for scalable performance.
• Intelligent Reporting and Close Automation: Template-based automated generation of regulatory and management reports, complemented by version control, data visualization, and one-click close processes.

Under an optimized setup, a traditional reporting cycle that previously required approximately 15 days can be progressively reduced to fewer than 3 days, delivering substantial gains in efficiency and management responsiveness.

The Strategic Value of AI in Actuarial and Management Decision-Making

The seminar further examined the long-term strategic implications of AI for insurers. By automating repetitive tasks, AI frees actuarial capacity and allows teams to refocus on analysis, interpretation, and decision support. In addition, AI’s pattern-recognition and deep-learning capabilities can surface risk drivers that are difficult to identify using traditional methods, enhancing the forward-looking nature of risk management.

From a business perspective, AI also enables dynamic pricing, personalized product design, and optimized capital allocation, helping insurers establish differentiated advantages in an increasingly competitive market.

Key Success Factors for Transformation

Speakers consistently emphasized that successful adoption of AI and automation is as much an organizational and governance challenge as it is a technical one. Critical success factors include:

• Executive Sponsorship and Strategic Alignment: Ensuring AI initiatives align with the company’s overall strategic objectives.
• Talent Development and Organizational Evolution: Building cross-disciplinary talent with both actuarial and AI capabilities.
• Data Governance and Infrastructure: Establishing high-quality, well-controlled data foundations as the cornerstone of AI applications.
• Risk Management and Compliance by Design: Balancing algorithmic innovation with data security and regulatory requirements.

Conclusion: From Automation to Actuarial Intelligence

The AIRC Seminar delivered a clear message: AI and automation are not merely tools, but powerful engines for transforming actuarial operations and management models. The true value lies not in whether AI is adopted, but in how deeply it is embedded into actuarial processes, model governance, and decision-making frameworks.

For insurers, this represents more than an efficiency upgrade—it is a transformative journey toward Actuarial Intelligence and sustainable competitive advantage.