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We all have a comfort zone at work. Topics which we could talk about until the cows come home.
WealthTech propositions are my bread and butter, they sit very comfortably in my ‘safe place’.
A number of new tools, ‘artificial intelligence’, ‘machine learning’, and ‘deep learning’, have rapidly crept into the WealthTech space.
These self-teaching systems are busy revolutionising many industries. WealthTech is no exception.
You need to ask yourself whether you want to jump on the train and work them into your ‘safe place’, or rather wait for the next train to pull in.
My fear with the latter is that you'll be late!
It’s true that artificial intelligence has been around for decades, but for the majority, it’s something we have typically reserved for the latest James Bond movie.
These tools crop up regularly in our research. I talk in length to various proposition owners about how these technologies are being implemented.
More often than not, they are on their ‘to do list’ and still to be added to their roadmap.
“I’ll get back to you on that Kerry”
“We can’t disclose anything at the moment, but we’re working on it”
There is a lot of chatter about these innovative technologies. A few firms are already rolling out such services in the real world. It's these that I want to take a look at today.
To the majority of us, it’s something new. You need a certain level of skill to understand how it works. Your brain needs to tick a certain way.
It’s not always important to understand the inner workings of these technologies and algorithms.
It is important however, to understand how they are going to change the way we design and deliver financial services.
Every now and then I speak to an organisation which turns everything on its head. This is exactly what happened when we met Hedgeable.
They are primarily a technology company. Having successfully developed and deployed its own digital wealth platform in the US in 2009, the company decided to open up its architecture and partner with organisations across the globe.
The technology consists of four key open application programming interfaces (APIs).
Partner organisations will use these open APIs to form the infrastructure / back end of their digital wealth proposition. The API’s are available on a modular basis so you can pick and choose which ones best suit your business.
Hedgeable’s co-founder, Mike Kane, described the process as ‘plugging in’ their API’s into your existing framework.
Once you have the back end in place, you can add on a number of optional customer facing modules.
One of these, being a truly fascinating artificial intelligence module.
The following video from the recent Finovate Spring conference gives a great feel for the system.
Digital Wealth Insights analysts are producing a more extensive analysis of Hedgeable’s functionality which will be available from this site shortly.
We recently reviewed Abaka’s intelligent savings proposition targeted at employers looking to improve the financial wellbeing of their employees. (see their full review here)
What makes Abaka different from other workplace propositions is that its powered by artificial intelligence and delivered via both a mobile App and a chatbot called AVA.
This technology collates and translates the employee’s data to provide insight and nudges into their personal financial life. It also provides comparisons of their financial life against that of their peers or others with similar circumstances as themselves.
Crucially, Hedgeable and Abaka are both LIVE physical propositions available to use in the UK today.
We are in an era where technology can take our data, read it, understand it, and provide us with proactive insights to make changes before reaching a particular unhealthy situation.
Over the course of the next few months I’ll be looking at ways in which artificial intelligence is changing the way we develop and deliver financial services.
I highly recommend getting on the artificial intelligence train sooner rather than later. Step out of your comfort zone.
It’s incredibly exciting!
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For me the outstanding demonstrations of the second day came from companies who were really throwing down the gauntlet to challenge conventional thinking and showing services that could transform major parts of the financial services industry in a very few years. These almost all focused on making more use of non-traditional forms of data to improve customer services, outcomes and achieve greater profitability.
When considering these in this context it is important to recognise that Finovate rules preclude firms showing vapourware service that are not yet built. While many offerings presented are still in their early stages slides and videos are prohibited, you have to show real software.
From the perspective of most traditional financial advisors the session from Hedgeable AI will be the proposition they will consider most controversial. Co-founder Matt Kane predicted that we will not need financial advisors for most of their current tasks within a few years. While presenting his artificial intelligence bot Katana he said he believes this will be capable of doing anything a financial advisor can do.
As part of the presentation Kane referenced a recent study identifying that as many as 61% of all jobs in the US financial service industry could be replaced by bots. Speaking to CTO Sid Sharma after the show he suggests you don't need advisors to do most of what they do right now, but accepts they are needed for more complicated stuff, “not for things like to checking to see if you are on target to meet your goals or selecting portfolios”. He identifies that “trust needs to be established but who said it had to be by a human”. At this point I suspect that there will be some readers of this blog on the verge of cardiac arrest.
My own perspective is that while eventually I do see financial planning and advice as services that within a couple of decades will be fully automated, and indeed virtually invisible I believe that point is at least a decade and a half away. However, when you see what Hedgeable have achieved the timeline does become questionable.
In the demo Hedgeable showed the bot identifying the maximum level of contribution a person could make to an IRA and the sources from which funds can be taken to pay such contribution. It could also present information to consumers to alleviate customer concerns if the market turns downwards. Perhaps the most sophisticated solution they showed was the ability of Katana to use personal financial management aggregation to examine an individual’s monthly expenditure and identify savings they could achieve in order to have money they could invest.
It might be tempting to dismiss Kane’s views, but having spent a couple of hours with his Co-founder and brother Mike together with Sid Sharma in London recently looking at their technology stack, Kane deserves to be listened to.
Using the experience they have gathered over the last six years involving tens of thousands of web chats and combining them with artificial intelligence the company can understand customer behaviour in ways most advisors and financial institutions can only dream of. They can provide a far better understanding of on-boarding, customer retention and support needs.
By understanding how much money people will bring with them companies can improve the targeting of offers relating to what customers want. Equally they can identify the behaviour that might mean a customer might be likely to leave and how much support they will need to keep them happy. This can enable organisations to plan resources for more effectively and where practical take cost out of call centres and other functions.
Another hugely impressive presentation came from Brian Lay the CEO of Alpha Rank who created social graphs to help businesses understand the key influencers of their customers. This involves the data that is considered to be the most valuable element of social media services like Facebook Twitter and LinkedIn. Apparently until 2012 Facebook disclosed this information with third parties however it has subsequently been decided to be too valuable to share.
Understanding these social graphics can enable businesses to predict when customers might be likely to leave and take preventative action. The same techniques have been used highly successfully in the mobile phone industry with T-Mobile reducing its attrition rates by 50% in a single quarter. In financial services the company will take 2 to 3 years of banking data, analyse it and provide maps back to their clients’ so they can understand who the crucial influencers are who drives behaviour amongst other customers.
The service focuses on off-line word of mouth recommendations which account for 93% of such conversations. By definition being off-line these are the hardest to measure but Ley identifies them as the most valuable form of endorsement. The service does not yet currently use social media data but this is seen as a natural further development.
One company already making very creative use of social media data Neener Analytics primarily focused on the lending industry and improving their underwriting decisions the service can also be used to improve insurance risk decisions. In each instance services are intended to compliment rather than replace processes.
The service is currently built around the data that can be extracted by consumers’ giving permission to access their Facebook, Linkedin and Twitter accounts and can be particularly valuable when trying to assess people who have a poor credit score simply because they have not previously used much credit. One major attraction is that the customer only has to give a single click consent to access this data and the service conducts the rest of the analysis.
While the company are prioritising credit and insurance risk talking to founder and CEO Jeff LoCastro I couldn’t help but be drawn to question how much such a service could be used to conduct attitude to risk assessments. This might be particularly useful in areas such as auto enrolment where savers will have had little experience or risk profiling and may find a single click approach more appealing as a process. Current risk profiling techniques do leave much to be desired and are an area where an improved customer experience might be very valuable. Finally I want to give a mention to Horizn who really impressed me with their gamification of training services that can be used with either internal staff or customers to encourage them to learn how to get the best out of new technology services. The platform provides training content with badges and reward points offered to users’ who complete brief online courses on how to use digital services.
Failure of staff to engage with and embrace technology training is one of the primary reasons that projects fail to deliver their intended return on investment. Providing reward through this platform could easily become a self funding proposition by increasing the successful adoption of technology. Although initially designed for internal staff one major bank has recently made the service available to 16 million of its customers.
I can immediately think of several ways in which this could be used within the life and pensions industry to help staff make more use of technology provided but also to provide valuable additional support to customers.
Notably, and I would say very deservedly, both Hedgeable AI and Alpha Rank won prestigious "best in show" awards.
Arguably the biggest story from this Finovate event are the changes being made to their future shows. From the September show in New York Finovate is now a four-day event with the initial two days of seventy plus seven minute demos supplemented by two days for detailed meetings and analysis. In addition elements of the shows will now be streamed so that it will be possible to focus on specific areas of interest. Both of these are really worthwhile changes. Having personally attended some 14 Finovate shows around the world I have always found them time well spent but these additional changes will make them even more valuable.
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Our income, expenditure and debt define how we live our lives today, tomorrow and ultimately into future. Is it not crucial therefore to ensure that every person gets the management of these fundamental processes right before approaching any actual investment, protection or retirement advice?
The Office for Budget Responsibility identified that credit card debt in May 2016 was £2,397 per household. Based on an average interest rate and minimum payment this would take 25 years and 6 months to repay. It also suggested that consumer borrowing trends will result in most UK households spending more than they earn for the rest of the decade.
The savings landscape is changing. There is no longer any reason for savings to be regular or centered on a single long-term goal now that there are micro saving tools to encourage consumers to save little and save often, as and when funds become available.
Of all the propositions I analyse on a daily basis there are very few with which I actively engage. Moneybox (www.moneyboxapp.com) is an exception. It is a great example of using micro savings to contribute towards an ISA or Investment account. I was able to save £94 in six weeks simply by rounding up my daily spending transactions to the nearest £1.
Cleo is another example of a Chatbot (www.meetcleo.com) budgeting tool which I use regularly. Instead of logging into my on-line banking or trying to make sense of a pie chart, I am able to send a simple text to Cleo to get the information I need instantly.
Whilst these two tools don’t have all the optimal functionality, they actively help me save and move towards a better financial life.
To provide a truly effective automated proposition it is crucial that we focus on helping the consumer build their financial life from the ground up.
We would encourage those organisations building automated financial service propositions to think of the end consumer and the journey they need to take in order to get in a position to actively engage in long term savings.
Further access to our analysis can be found at www.digitalwealthinsights.com (currently in beta). We aim to provide our audience with objective comparisons alongside our own informed analysis of automated financial service propositions, market trends and clarity around topics which typically create confusion.
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