New AI prediction tool from Mercer to assist UK DB pension plans

The new artificial intelligence tool from Mercer will assist defined benefit pension schemes anticipate the outcome of an options exercise

A new artificial intelligence tool from Mercer will assist UK DB pension plans anticipate the outcome of an options exercise. By using AI and data driven insights, schemes will hope to predict the chance of each individual member accepting a particular offer.

According to the 2019 incarnation of Mercer’s Asset Allocation report, 73% of UK DB pension plans are cash-flow negative, up from 66% in 2018. This means that for three out of four DB schemes, the amount of benefits paid out annually is higher than the amount of new contributions received.

Cash-flow negative DB plans require proper management in order to meet cash-flow and collateral needs – but at present that is much easier said than done. Mounting evidence of overextension of credit, possible liquidity implications as central banks rein in their market involvement, and continuing political fragmentation, all mean investors should consider effectively positioning their portfolios to weather possible market volatility.

In order to help DB pensions schemes with this complex process, Mercer has launched an artificial intelligence (AI) powered tool that can help DB pensions schemes predict the outcome of a member options exercise. In options trading, "to exercise" means to put into effect the right to buy or sell the underlying security that is specified in the options contract. If the holder of a put option exercises the contract, then they will sell the underlying security at a stated price within a specific timeframe.

Using anonymised data from completed member options exercises and the scheme’s own data, the machine learning algorithm determines the probability of a member accepting a tailored offer. This new data driven approach helps pension schemes and sponsors better manage risk through planning member options projects that have optimum member offer structures.

Mercer’s AI tool draws on anonymised data from over 20,000 member transactions who have been through an ETV exercise. From this, Mercer can support clients make better choices and help solve issues with DB pension arrangements, with the AI member choice tool identifying several factors impacting a member’s decision on whether to transfer out of their DB scheme.

A release from the firm explained that age is one key factor, with those members older than 55 on average 18-20% more likely to accept an offer than younger members. Place of residence also plays a determining part, with overseas members 10% more likely to accept a transfer value than those based in the UK. The time of year the offer is presented also impacts a member’s decision, with more responding to offers in spring. Mercer’s tool will continue to evolve as data from more completed exercises are added.

Andrew Ward, Partner and Head of Risk Transfer at Mercer commented, “Transfer options have played an increasingly important role in pension schemes’ risk management. With gilt yields at historic lows, members may be balancing taking higher transfer values against the backdrop of uncertainty caused by Brexit. However, while market conditions can influence take up, the decision to transfer is more likely to be driven by personal circumstances. By using AI and data driven insights, we can now help schemes predict the chance of each individual member accepting a particular offer.”