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On July 23, 2026, James Kang, CEO of AiNOS, visited RNA Analytics' headquarters in Seoul to sign a strategic Memorandum of Understanding (MOU) with Harry Kim, CEO of RNA Analytics.

Authored by Jihyun Kim, Global Sales Executive RNA Analytics

Enterprise-Perspective Pricing

Through the previous two articles, we introduced the need for a distributable-earnings metric that can represent the shareholder perspective — rather than the CSM metric alone — when pricing insurance products, and the Nested Scenario modeling methodology that can implement an appropriate projection of distributable earnings under the current IFRS 17/K-ICS regime.

Authored by Jihyun Kim, Global Sales Executive RNA Analytics

In the previous instalment, we discussed the problems of examining only the CSM metric when pricing insurance products, and the need for a shareholder-profit-perspective pricing metric. We noted that the concept of traditional VNB is useful as a way to evaluate distributable earnings — a shareholder-profit concept — but that it cannot be used as is and must be improved to fit the requirements of the current regime.

Hosted by Herald Biz

We are honoured to announce that RNA Analytics has been recognized with the award in the Insurance Solution category at the 2026 Korea’s Most Respected Companies, Institutions, Leaders & Consumers’ Favourite Brand Awards.

PowerKorea magazine feature

Actuarial science has a 200-year history; and it's finally getting a modern, AI-driven revolution.

Insurance has always been an incredibly data-intensive business, requiring companies to model decades of complex variables, from interest rates to long-term liabilities. But with global standards like IFRS 17 and evolving risk regulations, legacy systems are struggling to keep pace with the complexity.

Authored by Jihyun Kim, Global Sales Executive RNA Analytics

In 2023, IFRS 17 was fully introduced. Changes to the accounting regime inevitably affects corporate decision-making. Following the adoption of IFRS 17, insurers came to emphasize the CSM metric, and this influenced pricing as well, with insurers adopting the new-business CSM margin as a metric for pricing decisions.

Authored by Jihyun Kim, Global Sales Executive RNA Analytics

If an insurance company were compared to a car, insurance-product pricing would be its engine — for the driving force behind an insurer’s growth originates from its products.

R3S GIP simplifies sophisticated predictive analytics, allowing users to perform detailed pricing analysis with ease. Driven by continuous innovation, the platform bridges the gap between raw data and actionable underwriting decisions through a streamlined, user-friendly interface.

Authored by Sunil Yoon, Principal Actuarial Consultant RNA Analytics

As the field of Data Science continues to develop rapidly, many companies are showing increasing interest in modeling using Machine Learning or Deep Learning and are applying these technologies in their actual business operations.

Our CEO Harry KIM, joined Seoul Economic Network TV for the filming of “Jo Young-gu’s Hot Trend” in Seoul.

Authored by Angie Edmunds, Senior Actuarial Consultant, RNA Analytics

The non-life insurance industry is dealing with growing reporting demands, increasing data volumes, and more complex actuarial processes than ever before.

In the non-life insurance sector, volatility is part of the business. From catastrophe exposure to evolving claims development across motor, property, and specialty lines, insurers are under increasing pressure to deliver faster insights, stronger governance, and more agile regulatory reporting.

We are delighted to welcome Jihyun Kim to RNA Analytics Limited as Business Head, AP.

With more than 20 years of experience in the insurance industry, Jihyun brings deep expertise across actuarial, financial analysis, business planning, risk and capital management, product development and marketing.

Authored by Manuel Montes Senior Actuarial Consultant, RNA Analytics

Did you know that with the Profiler functionality in R3S Modeler, you can precisely identify which components and variables consume the most time during your model's execution?

In an era defined by volatile global markets and unpredictable operational shifts, the traditional approach to risk management is rapidly becoming obsolete. Organizations can no longer rely on legacy systems to anticipate the challenges of tomorrow.

Authored by Tak Lee, GC Regional Manager, RNA Analytics

In the evolving landscape of insurance regulation, the Own Risk and Solvency Assessment (ORSA) has transitioned from a mere compliance exercise to a critical pillar of risk management strategic decision-making.

Compliance with IFRS 17 may for some time have seemed like a distant (and moving!) target on the horizon, but now that the accounting standard is upon us, work to implement the new rules and models has come to fruition for many insurers, giving us the opportunity to dissect projects end-to-end, and to share best practice.

The shift to VM-22 represents the most significant change to U.S. annuity reserving in decades. By moving away from the prescriptive, formulaic CARVM approach, the new Principle-Based Reserving (PBR) framework requires insurers to model complex, stochastic future states.

¿Sabías que puedes integrar tus reportes web directamente en R3S Modeler?

Disponible desde la versión 4.1, R3S Modeler incorpora una funcionalidad poco conocida pero que puede aportar un gran valor añadido a los equipos actuariales y de riesgos: la pestaña Report, disponible en el workspace de resultados tras la ejecución de un modelo.

RNA Analytics, a global leader in actuarial and risk management software, is pleased to announce its official Gold Sponsorship of the Instituto de Actuarios Españoles (IAE) for 2026.

We are delighted to introduce Antonio San Román as the new Country Manager for Spain at RNA Analytics. Antonio joins us at a transformative time for the European insurance sector, bringing a wealth of leadership experience and profound knowledge of the local Spanish market.

With the rapid rise of generative AI such as ChatGPT, AI Agents have become one of the most prominent technology trends. Unlike traditional programmed tools, AI Agents understand natural language, converse with users, interpret intent, and autonomously execute tasks. Because of these capabilities, big-tech firms are deploying AI Agents to drive operational efficiency and organizational redesign, and this momentum is spreading across industries.

Authored by John Bowers, Actuarial Product Director, RNA Analytics

The past year has been a truly fascinating one for insurance actuarial professionals. Actuaries around the world have spent much of 2025 navigating complex and evolving regulatory frameworks, integrating artificial intelligence and machine learning into traditional work, developing climate risk expertise, and managing the gap between technical actuarial skills and the need for strategic business advisory capabilities.

Authored by James Beck, Senior Strategy Advisor, US, RNA Analytics

The US insurance landscape is undergoing a profound transformation. New regulations like Valuation Manual (VM)-22 for annuities, coupled with the global push for standardization embodied by the Insurance Capital Standard (ICS), are demanding an unprecedented level of sophistication from insurers.

In the insurance industry, professionals—including actuaries—process raw data to derive a wide range of analytical results that inform key business decisions. Consequently, many organizations are actively studying ways to enhance pricing models through AI technologies such as Machine Learning (ML) and Deep Learning (DL). However, in practice, the stage that consumes the most time is often not advanced modeling itself, but data preprocessing.

Artificial intelligence is offering US insurers a plethora of new opportunities, driving transformation in risk assessment, customer engagement and operational efficiency. Despite recent efforts to bring AI regulation under federal auspices, oversight remains characterised by a fragmented and largely state-driven regulatory environment.