Buzzword Buster – Beta Version

A beta (or beta version) is a term used to describe a version of software which is not finished yet. More specifically, it is usually a version which is shared with a small number of users for testing. The idea is that real users will inevitably turn up more issues than any test script will, and thus it is good to test products with a, not insignificant, number of users.

Beta’s are often shared too early and too widely by over enthusiastic founders and evangelists in an attempt to use them as pseudo marketing tools. This can lead to statements such as:

“Yah, I know it is buggy as hell and you can’t sign in and when you press f5 the world sets on fire, but it’s a beta though so that’s all ok. First to market, yo.”

Weirdly, although people bang on about their beta version constantly, they rarely talk about their alpha version. The alpha really is just, the live product that in theory doesn’t make the world explode or make your grandma cry.

 

Disagree or want to add something? What does a beta version mean to you?

Robo-Advice is failing. Intelligent Advice is Next

Robo Advice is failing

Robo-advice has been a persistent buzzword recently and never seems to be far from the trade press even though much of the activity has been predictable repeats of the same old formula:

  1. Provide a risk assessment of some sort
  2. Drive consumer to risk rated portfolio of cheap passives plus a not-so-cheap discretionary charge
  3. Claim that this isn’t actually advice because you didn’t understand enough about the customer
  4. Rinse and repeat

 

Equally predictable is that the asset inflows and mass customer acquisition promised by robo 1.0 have failed to materialise. I can’t say I’m surprised by this, as there is a core failure at the heart of this model. Providers typically set out to sell rather than help their clients understand more about their financial lives and work out what is right for them.

There is also a more prosaic challenge around target market segments. I have long said that retail banks and scale brands are likely the only firms who can make a real success of the endeavour. Successful robo-advice requires a proposition which is fit to distribute to many lower net-worth clients while most of the nouveau discretionary managers are typically targeting the fertile but incredibly hard to attract high net worth segments.

On the plus side, behind closed doors, some organisations are beginning to embrace the fact that there is more to advice than just driving a decision to an investment portfolio. I have met with one established FS firm and a couple of start-ups recently who are approaching automated advice from the point of view of helping the customer understand their financial life to drive better outcomes. In all of those cases, I’m really excited to see the output (all of which should be in market in 2017).

Intelligent advice is next

But what next? If, on the whole, robo 1.0 has been a failure, what are firms doing to make the next wave of robo advice a success? There are a number of key threads one could pursue here. Integration with wider financial ecosystems, brand affiliations with non-FS firms, multipurpose messaging platforms like Facebook messenger and Wechat and the marrying up of investment advice with the wider universe of financial services all spring to mind but as a technologist, there is one that I’m even more interested by.

Artificial intelligence

Firstly, let’s get the basics right. Artificial Intelligence is a bit of a misnomer in its current usage. AI really means a system which is capable of the same kind of intelligence and thought as a human. We are a long way away from that on all fronts, but what we are seeing enormous inroads into are aspects of specific intelligence. i.e. computers doing certain defined tasks as well as (if not better than) a human.

Machines are getting smarter at getting smarter

There is one particular aspect of AI which could have a really big impact over the next few years, and that’s machine learning. Essentially the ability for predefined pieces of software to build on and improve themselves without the requirement for human intervention. I won’t dig into the details on how it works here, but there is potential for enormous disruption in the investments sector.

We have already seen firms like Bridgewater building AI into and these will certainly be based around machine learning. Financial markets are complex and changeable. By allowing a software system to modify itself based on direct feedback from the network, the hope is that over time, these systems become first as good as humans are at stock picking, and then better. One of the perks of AI is that, while human’s have a cognitive cap, in theory machines don’t, so they can get smarter and smarter at their jobs.

So if that’s the bleeding edge of asset management, how does this get connected back to retail customers? Well, financial planning ultimately is about matching consumers needs and goals to tax efficient products and identifying investment options which sit within these. If we have developed a system which gets smarter and smarter at playing the markets, there is nothing stopping us developing one which manages tax efficiency for investors. In fact, this represents a much easier challenge especially with PSD II on the horizon (regulation which will force bans to open up and share data about consumers).

Reading the regs

But what about the way regulation and tax law are applied to investment and product decisions? Currently, if you want to understand the full detail and implications of the various regulatory frameworks, sourcebooks and tax laws you have no option but to put in the hard yards of research, or hire in smart people who have already done that. In most cases, you end up having to do both.

The work that big tech firms (IBM ,Microsoft, Google etc.) have been doing around natural language processing means software can already understand human freeform speech and text and manage the translation of this across multiple language bases with a system which teaches itself how to continually improve what it is doing. It won’t be long before we have a system which can actually read the regulatory framework, understand the difference between rules and guidance, feed in the entirety of tax law and make interpretive decisions based on the full data set.

A different type of software

Throw in a few other related developments in the AI space, and we are rapidly approaching a point where we can move away from having to define siloed systems which deal with tax efficiency, investment picking and customer management. Instead, we will be looking to develop unconstrained self-improving systems which can learn about retail customers, and their differences from professional investors, ascertain which aspects of the regulatory framework would apply to their subject and execute a series of cash movements, product opening instructions and investment trades on their behalf, all the while, making sure it stays within the bounds of the firm’s regulatory permissions, or indeed, applying for new ones if necessary.

If you had absolute confidence that a tool would give you the correct recommendation regardless of context, then the jobs of advice and investment management change. Ironically, as time goes on, the challenges will become less about the technical capabilities (which will march onwards) but more around the regulatory construct (because NOTHING in the FCA handbook is based on regulating something which you don’t understand) and the risk appetite of firms to adopt such approaches.

Ultimately though, we could see the emergence of a genuinely intelligent advice. Delivered via computer algorithms for next to no cost, and solving the challenge of complexity which currently requires the employment of thousands of intermediaries, advisers and experts of all shapes and sizes. Of course, in this future vision of automation and AI, the human touch points for interaction will be vital in building empathy and confidence.

 

This article was originally published on Trustnet.com – https://www.trustnet.com/News/719339/adam-jones-robo-advice-is-failing–intelligence-advice-is-next/

 

 

Buzzword Buster – Insuretech

A subset of Fintech, InsureTech is a broad term to cover a range of tech start-ups and innovation within the insurance industry. There are a few genuinely innovative ideas in this space which cover some interesting key themes such as the personalisation of pricing, the reestablishment of mutual businesses, peer-to-peer and community initiatives and the use of smart contracts in the claims process.

In addition to these interesting ideas, here are also a huge number of tech firms who are essentially just doing good old fashioned insurance brokerage in a slightly different way to those before them. It is unlikely that many of these players will make a lasting impact on the industry.

 

Disagree or want to add something? What does Insuretech mean to you?

Buzzword Buster – Fintech

An amalgam of finance and technology, fintech does what it says on the tin. It was among the first of a huge range of portmanteaux adopted by start-ups from almost every industry. Healthtech, scitech, traveltech and regtech are other examples. I’m waiting for the advent of techtech to prove we have truly disappeared down the rabbit hole.

Fintech can serve as a useful term to group innovative start-ups working within financial services. Unfortunately the businesses are so diverse (from payments to robo advice, to insurance and trading systems) that the classification becomes so broad as to become all but meaningless.

The phrase has also been repurposed heavily by tech firms who have been operating in the financial services space for many years. For many of them, it is a nice PR friendly buzzword that suggests they are far more progressive and innovative than they actually are.

 

Disagree or want to add something? What does Fintech mean to you?

Buzzword Buster – Disruption

One of the most over used phrases in modern business. At its core, disruption relates to disturbance which interrupts events. In business the word is over used as a glorious proclamation that a certain company, product or idea is going to change the way an industry works. Oft cited examples are Uber and Airbnb.

In The Innovator’s Dilemma, Clay Christiansen breaks disruption into two main types, either addressing a market which couldn’t have been addressed before (new-market disruption), or offering a simpler, cheaper or more convenient alternative to an existing product (low-end disruption).

Innovators and entrepreneurs will often be overheard discussing how they have been disrupting all over the place. Venture capitalists will all too often have a portfolio which is stuffed full of disruptive startups. All too often this is little more than a good PR story to cover the fact that companies are producing slightly improved versions of products that already exist in the market.

Entrepreneurs who fail to be disruptive usually have a hard time making an impact on their markets. While big established (and well capitalised) brands can get away with gradual conservative improvements on existing product lines, tiny firms with no public presence will always struggle.

There is a really great overview on disruption in business, and The Innovator’s Dilemma, here.

 

 

Disagree or want to add something? What does Disruption mean to you?

Buzzword Buster – Funding Rounds

A general term applied to the stages start-ups go through to gain investment from venture capital firms. After obtaining seed capital, funding rounds are usually structured into a few chunks, sometimes called series. E.g series A funding.

When start-ups go through funding rounds they are trading capital in their business for money to help them run and grow the business and further develop products and prototypes.

Unfortunately for many start-ups, there can be a tipping point where they have taken on lots of venture capital funding but not yet grown the business to a size where a sale or significant profit are available to repay investors.

This is often caused by speculative over valuations of firms. Further VC investment at this point can be seen as a sign of faith from investors although it can also be a way of VCs saving face or the last hopeful punt on a doomed enterprise.

 

Disagree or want to add something? What do Funding Rounds mean to you?

 

Buzzword Buster – Seed Capital

Early stage investment in a start-up, prior to more formal funding rounds. Seed capital is usually provided by the founders themselves, their friends and family and other angel investors. It generally represents the first external injection of cash into a start-up.

Seed capital pays for a range of core activities including product development, customer testing and PR as well as providing salaries for early employees (although these are few and far between, and often on a temp contract basis). Because the amount available from seed funding is relatively low, it usually only provides a runway of a few months for start-ups to run their businesses for.

Seed funding has also been known to fund a series of ‘essential’ business trips around the world where founders try to sell their start-up idea to Venture Capital Firms and enterprise partners, enjoying nice hotels while they are at it.

 

Disagree or want to add something? What does Seed Capital mean to you?