Aligning admin systems in the pre and post-retirement worlds

For most pension scheme trustees and sponsoring employers, not running out of money means ensuring the scheme’s assets and investment strategy will cover the costs of providing regular pension payments from the day the members retire until the day pension payments cease.

Unfortunately, the task of adequately funding post-retirement liabilities is complicated due to the uncertainty created by three main influencing factors: investment returns, inflation and longevity.

If the return on investments is below expectations, it may force a change of investment strategy or the need for higher contributions.

Percentage increases to pension payments are often linked to inflation rates, meaning that a rise in the rate of inflation can cause the amount of benefits paid to pensioners to increase above original predictions.

Increased longevity can result in pensioners being paid more benefits than expected, as well as exposing schemes to greater risk from volatility in investment returns and inflation. 

Robust data

To adequately fund post-retirement pensions, the systems designed for making post-retirement investment decisions must model the variability and risk associated with the main factors of investment returns, inflation and longevity.

Deterministic models can be sufficient to quantify the liabilities for pre-retirement pensions. However, the only way to quantify the liabilities for post-retirement pensions effectively is through the application of a stochastic model that can simulate the complexity and future uncertainty involved.

It is not only the quality of the information, but also the ease with which that information is shared between the administration and investment world that matters.

In addition to modelling the future, post-retirement investment systems must also take account of the past, and this is where the importance of the relationship with pre-retirement pension scheme administration systems arises.

Whether liabilities are calculated based on aggregated cash flows or individual member benefits, the underlying data must be accurate to provide the best starting point for the future predictions about the scheme.

There are several other influencing factors on liabilities, relating to the choices and circumstances of pension schemes’ members, including cash commutation decisions and the details of any eligible dependants they have. So, the more information known the better the result.

Where there are gaps and unchecked errors in the administration data, it increases the likelihood that the liability information used to make investment decisions will be misrepresentative of the scheme.

Sharing information 

It is not only the quality of the information, but also the ease with which that information is shared between the administration and investment world that matters.

As economic factors are constantly in a state of flux and changes in administration data occur frequently, investment strategy risk management requires regular monitoring and review.

In ensuring that investment-world systems have the most up-to-date information on which to base the liability and risk analysis, the process of updating the system should be simple and easy to reconcile between repeated exercises.

Quite often, the mismatch between system interfaces and data structures causes considerable amounts of painstaking pre-analysis manipulation prior to the really valuable information being produced.

Bridging the gap between pre-retirement administration systems and the post-retirement investment world comes down to high quality inputs and efficient processes.

Robust processes provide readily available, rich analysis that can be used to achieve positive outcomes for members.

Meanwhile ensuring the data in administration systems is accurate provides the potential to frequently review investment decisions in a cost-effective way.

Tom Nimmo is business development manager at Veratta