There’s no doubt that long duration targeted improvements (LDTI) and principle-based reserving (PBR) have increased the complexity of actuarial modeling and reporting. What’s less certain is how to manage the impact on your processes.
Once again, modern technology comes to the rescue. With end-to-end automation, you can ensure compliance and focus on the bigger picture.
Capture the opportunity
Thanks to LDTI and PBR, some actuarial groups have been able to justify increased budgets and invest more in modern technology and modeling talent. That’s moving them closer to a world where model results are produced with minimal manual touchpoints and whole parts of the actuarial modeling process can be automated away. That, in turn, allows you to run more sophisticated actuarial analysis and reporting – and moves the role of the actuary from model processor to model storyteller.
Other insurers may not view regulations as a catalyst for a new approach to actuarial modeling; they may see them as just another set of regulatory requirements. These firms typically have tight budgets and resources. If you’re in this group, you won’t adapt your processes and will simply use existing workflows to produce LDTI and PBR results. The risk to this approach is, as regulations evolve there’s an increase in complexity and a need for more computational power.
It is likely that modernization is inevitable, the question is what will be the catalyst? Forecasting the need for resources or burning out existing ones?
Doing more with less
Whichever approach you’re taking, you have to do more with less. But as actuarial calculations, analyses and processes become more complicated, they’re pushing organizations to their technological and educational limits.
LDTI and PBR are an opportunity to re-evaluate your current actuarial modeling processes and imagine ways you could simplify them.
For example, would it make sense to consolidate different modeling functions to create a single, centralized modeling team? When teams are decentralized, you can end up duplicating many parts of a modeling process, which makes you less efficient at building and validating models, updating assumptions and upgrading systems. You’re more likely to produce inconsistent model results, as well.
Decentralized teams also tend to gravitate toward closed modeling systems. You can struggle to adapt to a changing actuarial reporting environment and adopt more powerful and flexible open code. With coding practices and standards often varying across actuarial groups, you’ll get more and more inconsistencies too.
And as the complexity of regulations has increased, so has the need for expertise and flexibility in actuarial modeling. Centralizing your modeling teams will go a long way to creating modern model experts with a deep knowledge of actuarial assumptions, regulations and processes.
Assessing your systems
As well as considering the consolidation of your teams, think about how many different systems you run in the modeling process. Ideally, you would have a single actuarial modeling platform that can help you not only determine reserves but also complete the projections that support pricing, risk and planning.
And if you run your system in the cloud, you can access the computational power you need, when you need it – no need to invest in costly new cores.
And with the introduction of PBR, this elastic infrastructure is ideal for calculating and projecting on a stochastic reserving basis, as well as supporting the vast number of simulations it takes to determine a principles-based reserve.
More broadly, an on-demand cloud service offers actuaries a more flexible work environment for completing regulatory exercises – without the overheads of purchasing more cores or hiring a costly internal IT team.