Your battlefield-tested partner for LDTI

Authored by James Beck, Senior Strategy Advisor, U.S. Region, RNA Analytics

RNA Analytics has deep experience of IFRS 17 implementation in Asia and Solvency II in Europe, building top-tier expertise that is standing by to deliver LDTI-ready tools tailored for the US market.

The US life insurance sector is in the midst of a perfect storm of regulatory and technological pressures. Amongst these, the Long Duration Targeted Improvements (LDTI) accounting standard remains a high-stakes challenge, with many mid-sized and larger insurers still grappling with data overhauls and model validations.

While LDTI became effective for most insurers' fiscal years beginning after December 2023, full implementation, especially for 2025 adopters, continues to present challenges.

The impacts of LDTI stretch far beyond even the headline-grabbing compliance costs, revealing organisational inefficiencies and constraints on actuarial and IT resources and growth – with many carriers turning to specialist consultancies for support.

Global reinsurance trends are further amplifying cross-border exposures, prompting insurers to adopt integrated tools that enhance capital efficiency and better manage volatility. And with Solvency II extensions in Europe and ongoing IFRS 17 challenges in Asia converging with LDTI timelines in the US, only truly borderless ALM, underpinned by sophisticated stochastic modeling, can handle the burden of multiple regime projections and risk transfer.

One of the core challenges of the new accounting standard lies in managing the mismatch between assets marked-to-market and liabilities that are now far more rate-sensitive. The fundamental changes to asset-liability matching strategies demand advanced and specialist technology and robust platforms – platforms that are fit for LDTI’s need for precision and accuracy. Updated cash flow assumptions now have to reflect current conditions quarterly, complicating liability discounting and exposing duration mismatches. Legacy systems also struggle with the required cohort-level granularity and historical inflows that need new sources. Addressing all these ALM complexities demands proven implementation frameworks.

At RNA Analytics, our decades-long experience with IFRS 17 implementation in Asia and Solvency II in Europe gives us a unique insight into the pinch points insurers face in grappling with the operational and technical hurdles of LDTI implementation. We have field tested dynamic ALM for volatility, created AI-enhanced actuarial modeling to cut manual processes and enhance automation, and developed modular integration for phased transitions – together providing ‘plug and play’ scalability for insurers struggling with execution. These struggles manifest in specific ways for mid-sized and larger insurers, including delayed quarterly reporting – sometimes by up to 45 days – due to data integration failures in cohort-level cash flow tracking for annuities; and for large stock life carriers, Securities and Exchange Commission scrutiny over inconsistent discount rate applications across variable annuities, which can lead to both restatements and higher audit fees.

De-risking LDTI implementation

Insurers looking to derisk LDTI implementations are benefiting from the pragmatic lessons learned by fellow carriers across the world, leveraging their experience of implementing IFRS 17 and Solvency II.

According to KPMG’s 2023 report, Benchmarking LDTI Implementation, 2025 adopters of LDTI reported implementation timelines ranging from 18 to 24 months for the full rollout, including data migration and testing. Compared with these averages, RNA customers will typically experience a 30% faster implementation – achieved through automated cohort modeling and pre-configured disclosures. This can be validated by similar durations observed in IFRS 17 projects, where set-up time was reduced from an average of 20 months to just 14 months.

Further case studies highlight quantifiable – and transferable – gains. Our Korean life insurer customers implementing R3S Dynamic ALM, for instance, achieved 25% faster stochastic runs and a 15% reduction in capital charges via optimized asset allocation in 2024; while European composite carriers cut ALM reporting cycles from quarterly to monthly, improving risk adjustment returns by 8%. Meanwhile, many of our customers benefit from our R3S RiskPlatform, which uses sophisticated nested stochastic modeling to automate the entire risk modeling process, from projections to stress testing, seamlessly linking each sub-model without requiring any model code changes.

RNA’s success is rooted in a deep understanding of the varied nature of insurance businesses today, and a recognition that no two are the same. Our flexible approach is mirrored in our flagship R3S suite's modular, open architecture design, allowing customization – without vendor lock-in. By taking an holistic approach that starts with a diagnostic audit of existing data and models, before layering targeted modules, we can ensure end-to-end ALM from projection to disclosure. This streamlined method delivers visual scripting and API integrations that simplify set-up by automating 70% of data flows. Transparency is critical for regulatory compliance and enterprise governance today. As such, open-source-style visibility into assumptions and runs enables auditors buy-in and internal governance with real-time dashboards for scenarios testing.

By leveraging our vast experience in actuarial and financial analysis across Asia and Europe, RNA is uniquely positioned as a battlefield-tested partner for US insurers implementing LDTI, with best practice embedded across a suite of proven tools to accelerate compliance without operational disruption.