Research by Broadridge indicates that by 2027, total accumulated assets within Master Trusts (MTs) will exceed £368bn and MTs will account for 78% of the trust-based market. Due to ongoing consolidation, we can expect there to be around 20-25 MTs in the next five years – ranging from commercials, boutiques, not-for-profits and industry specific arrangements like the Railways Pensions Scheme. And with such anticipated growth and consolidation, one might ask how innovation for member engagement could and should develop through to the next decade?

The current problem with member engagement today:

Member engagement can mean different things to different schemes as well as individuals. For today, let’s assume that good member engagement means that members are engaged at the right times throughout their lives, thereby taking appropriate action via the most suitable routes in order that the scheme can be well managed and importantly that members can attain the best possible outcomes for their financial future. However, lost pots, proliferation of deferred pots as well as habitual inertia amongst us humans to put off what we know is important but feels too far away in the future, (or perhaps because it’s not easily accessible and at times confusing) means that the right level of member engagement continues to be elusive. However, the future could hold the key.

Big Data, algorithms and AI for better member engagement:

Pensions, it seems, have been somewhat immune when it comes to big data and AI in comparison to what we are already experiencing in other consumer facing sectors. Therefore, this makes our first and central prediction a little more predictable. Within the next five years, machine learning combined with behavioural science will revolutionise how members engage with their pensions.

In terms of pensions and member engagement, the right tech could be better used to nudge an individual in the right way (for them) when there is an anticipated or actual change, otherwise termed as a “life moment”. This impending life moment, although significant, may not always be recognised by the individual as having a financial impact nor that a particular action is required to make financial adjustments. Artificial intelligence, big data and behavioural science will enable more individuals to (1) recognise that a financial change is required or due (2) use motivational language and tools specific to them and their behaviors and (3) make the process of taking action much easier.

For some years, the ability for Google to diagnose an unknown pregnancy has been widely discussed. So it doesn’t take much imagination to see that tech will recognise that an individual has encountered a “life moment” such as getting married, having children, changing jobs, losing a spouse, reaching a certain age or indeed receiving some unexpected money. If we take “getting married” as an example life moment, the member would receive an alert via their chosen media in a language and style that motivates them to consider that a change of beneficiary is required, and perhaps a review of contributions too. There could be an assessment on their married peers of similar socio-economic or demographic factors to alert the member as to how their financial future is tracking and depending on that what action is required to engage with scheme to make the necessary tweaks. Crucially, the language and information used to motivate the individual will be tailored to their personality. For example, if the member is categorised as a particular personality type that likes to keep up with their peers, the message to entice action could be, “9 out of 10 of your school friends are saving x% into their pension and they can anticipate to retire with £X amount”. Through automated monitoring and AI, the machine will also learn from reactions and adjust the messages accordingly in order to get to continually deliver messages that are personally motivational to the member in order to drive the right type of action and engagement.

AI and behavioural science will result in nudges and auto-escalations that are personalised dependent upon sophisticated segmentation and help deliver improved member engagement where arguably there isn’t much other data and where we know people are just not interested.

The rise of ESG

Demand for transparency of investments and responsible ESG investing labelled as environmentally conscious and ethically-aware, is rightly on the rise and proving to be a key area of differentiation for MTs. Over the next five years, technology and scale should enable more agile, responsible and transparent investment strategies. Campaigns like Make My Money Matter will have members thinking “I can support causes close to my heart with my money right now while saving for my later life.” As money that matters means engagement, members are going to demand the technology that enables them to monitor and assess

information on a range of factors and not just performance. Transparency to any changes to eco-friendly or diversity policies (just as an example) will be key – and the recent Boohoo scandal demonstrates this requirement.

The power of default inertia

Glidepaths as we know it might not be suitable as retirement is no longer a fixed date but a blurred ongoing and changing process. This means that member engagement is even more important. Advancements in technology and the role of the “default” could mean that what was

the scheme default move towards being the member’s default for their particular life stage, requirements and attitudes. This personalised default could be based on big data collected via browsing habits, bank data etc. which through applied behavioural science is continuously developed via machine learning. If we accept that a human’s innate inertia for pensions engagement is not going to change, how can we use that power of inertia to help automate what is required of their default or indeed contributions and so on? Through technology, we could get to a stage whereby normal day-to-day activities and occasional, but important life moments are automatically monitored and fed through to a default (managed within a framework), so that smart changes (to contributions for example) can take place without the member having to ‘engage’ in the traditional sense.

The foundations:

Pensions Dashboards None of the above can happen without the delivery of pensions dashboards. That is not to say that the above technology will only be available via a pensions dashboard, but that for such smart technology to work across the board, all schemes and all pensions must be brought up to a recognised and agreed standard for surfacing data that is of value to the individual. Although promised for delivery in 2019, one must surely hope that within the next 5 – 10 years, our country will have a valuable Pensions Dashboard. It is this project’s founding technology (particularly for finding lost pensions, identity verification, data standards and governance) that will enable smart member engagement of the type mentioned above. Pensions Dashboard will open pensions to 21st century consumer technology.

This is and will be no easy feat. Despite some commentators drawing parallels, pensions dashboards harbour a very different set of challenges to Open Banking namely (1) Identity (authentication) and (2) the growing issue of lost and deferred pots.

Unlike the small number of banks, there are thousands upon thousands of schemes a member could attempt to contact in order to find their lost pension – it is estimated by the ABI that there are about 1.6 million lost pots (totally nearly £20bn and that’s just in the DC space). Auto-enrolment (AE) is also adding another layer of complexity with the onslaught of small pot proliferation and associated administration costs. Research from PPI anticipates 27 million smaller pots could be in MTs by 2035. There is a total of 10 million small deferred pots currently costing around £130m each year in administration, with the PPI estimating the 15-year bill for servicing the extra 17 million pots (in 2035) to cost half a billion pounds. This means that every active member in an AE pension is currently supporting an inactive one, and the figure is likely to increase to 1:3 in the next 15 years. This proliferation is costing everyone and must be addressed.

Pensions Dashboard will only go some way to solving this future problem, but importantly it will deliver a solid foundation upon which pensions data suddenly becomes accessible. For the first time, individuals as a minimum will be able to find all of their pensions and their values. By combining this newly surfaced and accessible pensions information with machine learning, AI and behavioural science, we can expect to see a significant rise in member engagement – automated or not.

Known knowns, known unknowns and unknown unknowns

Although 2020 will probably go down in history as one of the most unexpected and challenging year, Covid-19 is forcing us to re-evaluate just about every corner of our lives. One could argue it is a known unknown that has proven to also open opportunities and in particular the role of technology, innovation and the power for remote engagement. It has accelerated adoption of technology such as Zoom and other video conferencing facilities to the point where over a space of a few days or weeks it became the norm for engaging with colleagues and family.

With the information we have and what we know now, I think we can safely predict a positive change in the level of member engagement and that this engagement might not necessarily be in the traditional sense. There is no doubt that digital innovation, big data and smarter investment strategies, at both the accumulation and at-retirement/decumulation stage will revolutionise member engagement over the next 5 – 10 years – and not just for MTs, but across the entire pension spectrum.