Next-generation risk management
RNA Analytics has the technology tools and expertise to support insurers in delivering smarter and faster reporting. Seokho Woo, consulting business executive at RNA Analytics, and Justin Hwang, the group COO and vice president, explain
How is the actuarial modelling and risk software landscape evolving in response to increasing demand for real-time data and faster reporting cycles?
Seokho: The actuarial and risk management software landscape is evolving rapidly to meet insurers’ growing demand for real-time data, faster reporting cycles, and integrated risk insights. Traditional static models are being replaced by dynamic platforms capable of continuous monitoring, scenario testing, and on-demand reporting. RNA’s R3S RiskPlatform exemplifies this evolution by automating the end-to-end closing and valuation process, integrating existing models, and consolidating fragmented data, calculation systems, and reports within R3S Workflow Manager.
Insurers also use GRC platforms alongside R3S to centralise model governance, regulatory controls, and risk reporting, ensuring complex actuarial outputs feed seamlessly into enterprise-wide compliance, audit, and risk management processes. The platform’s workflow includes embedded verification checks at each step, enabling single-click report generation without manual intervention.
What advances has RNA Analytics made in actuarial modelling and risk management software over the past year? Seokho: Over the past year, RNA Analytics has strengthened R3S RiskPlatform to meet evolving actuarial and risk management demands.
Enhancements include the integration of existing closing and valuation models, automation of the end-to-end reporting process, and expanded support for IFRS 17, Solvency II, ORSA, and risk dashboard reporting.
By consolidating fragmented data and calculation systems, the R3S Workflow Manager allows single-click report generation with embedded verification, reducing manual effort and improving the accuracy and trustworthiness of outputs. Insurers also pair R3S with GRC platforms to centralise model governance, regulatory controls, and risk reporting, delivering stronger compliance, operational efficiency, and clear auditability across finance, actuarial, and risk functions.
Additionally, the R3S Risk Dashboard enhances traditional financial reporting by integrating forecasting models, historical data analysis, and sensitivity testing to identify potential risks.
Which areas of actuarial and risk management work at insurers are most likely to benefit from artificial intelligence (AI) or machine learning? Why?
Justin: Two areas, in particular, stand out: pricing and automation of actuarial workflows.
Pricing is the area where actuaries can most easily apply machine learning / deep learning techniques. For example, in auto insurance, pricing is traditionally performed using statistical models applied to large-scale input data. Until recently, the Generalized Linear Model (GLM) has been the dominant approach.
However, by supplementing GLM with machine learning models or deep learning models like CANN (Combined Actuarial Neural Network), actuaries can compare and analyse results more objectively.
Another major benefit lies in automation. By the nature of their work, actuaries handle large volumes of numerical data, often relying heavily on Excel or databases. For example, in East Asia such as Japan and Korea, where insurers offer a vast number of complex insurance products, actuaries spend substantial time on manual tasks including pre-processing input data and building models in actuarial software.
At RNA Analytics, we have already developed an AI Agent designed to automate these workflows, with pilot projects planned for a company in Korea or Japan. In addition, we are preparing to launch a Copilot for our R3S Modeler, RNA's cashflow projection software early next year. This Copilot will act as a sort of virtual actuarial modelling consultant, assisting actuaries in real time and enhancing both efficiency and accuracy in model development.
How can insurers balance the use of AI-driven techniques with the need for transparency and explainability in regulated actuarial processes?
Justin: Insurers can best balance the use of AI-driven techniques with the need for transparency and explainability in regulated actuarial processes by integrating modern AI technologies, such as LLMs, with traditional actuarial modelling. While AI technologies like LLMs can generate necessary future cashflows for actuaries, their "black-box" nature inherently lacks the transparency and explainability required for regulatory scrutiny.
RNA Analytics addresses this challenge by building the core actuarial models for cashflow calculations within R3S Modeler. This allows internal users and external auditors to thoroughly review and validate the models, ensuring full transparency and explainability.
