FinTech Exchange, Chicago

Now in its fourth year, Barchart’s FinTech Exchange* event seems largely designed to address the specific needs of the Chicago trading community: technology and data vendors; brokers and intermediaries; and commodities, futures and derivatives markets – with an emerging thread of Blockchain and crypto.

In fact, the Keynote Speaker, Dr. Richard Sandor, spoke of Blockchain as being as significant as the invention of double-entry bookkeeping, the launch of stock markets, the introduction of electronic trading, and the creation of financial derivatives combined.

Other topics included: the evolution of global financial markets; the threat or potential of enterprise Blockchain and FinTech solutions; the role of cryptocurrency exchanges; understanding big data and data analytics; deploying AI and machine learning within FinTech; and the rapid expansion of API solutions as products and services in their own right (not just as a means of data delivery).

There was also a panel discussion with the winners of the previous day’s Startup Exchange pitch event.

On behalf of Brave New Coin, I ran a series of round-table discussions on the current state of cryptocurrencies, token sales and digital assets; and the prospect of so-called security tokens (a topic which is sure to feature in this blog in coming months).

Finally, the notion of “alt data” is gaining attention, and not just among hedge funds. In part a by-product of big data (how to make sense of all this data), alt data is set to become the high-octane fuel for generating yield (if data is the new oil).

* Declaration of interest: Barchart syndicates Brave New Coin news and technical analysis content

Next week: Corporate purpose, disruption and empathy

 

Big Data – Panacea or Pandemic?

You’ve probably heard that “data is the new oil” (but you just need to know where to drill?). Or alternatively, that the growing lakes of “Big Data” hold all the answers, but they don’t necessarily tell us which questions to ask. It feels like Big Data is the cure for everything, yet far from solving our problems, it is simply adding to our confusion.

Cartoon by Thierry Gregorious (Sourced from Flickr under Creative Commons – Some Rights Reserved)

There’s no doubt that customer, transaction, behavioral, geographic and demographic data points can be valuable for analysis and forecasting. When used appropriately, and in conjunction with relevant tools, this data can even throw up new insights. And when combined with contextual and psychometric analysis can give rise to whole new data-driven businesses.

Of course, we often use simple trend analysis to reveal underlying patterns and changes in behaviour. (“If you can’t measure it, you can’t manage it”). But the core issue is, what is this data actually telling us? For example, if the busiest time for online banking is during commuting hourswhat opportunities does this present? (Rather than, “how much more data can we generate from even more frequent data capture….”)

I get that companies want to know more about their customers so they can “understand” them, and anticipate their needs. Companies are putting more and more effort into analysing the data they already have, as well as tapping into even more sources of data, to create even more granular data models, all with the goal of improving customer experience. It’s just a shame that few companies have a really good single view of their customers, because often, data still sits in siloed operations and legacy business information systems.

There is also a risk, that by trying to enhance and further personalise the user experience, companies are raising their customers’ expectations to a level that cannot be fulfilled. Full customisation would ultimately mean creating products with a customer base of one. Plus customers will expect companies to really “know” them, to treat them as unique individuals with their own specific needs and preferences. Totally unrealistic, of course, because such solutions are mostly impossible to scale, and are largely unsustainable.

Next week: Startup Governance

 

StartupVic’s Machine Learning / AI pitch night

Machine Learning and AI are such hot topics, that I was really intrigued by the prospect of this particular StartupVic pitch night. First, this was a chance to visit inspire9‘s recently established Dream Factory – a tech co-working facility, maker space, and VR lab in Melbourne’s western suburb of Footscray. Second, the Dream Factory, housed in a landmark building owned by Impact Investment Group, was a major beneficiary of LaunchVic funding, and this event could be seen as a showcase for Melbourne’s tech startup sector. Third, with so many buzzwords circling AI, it offered a great opportunity to help demystify some of the jargon and provide some practical insights.

Image sourced from StartupVic

Instead, the pitches felt underdone – probably not helped by the building’s acoustics, the poor PA system, and the inability of many of the audience to be able to read the presenters’ slides. I wasn’t expecting the founders to reveal the “secret sauce” of their algorithms, or to explain in detail how they program or train their “smart” applications. But I had hoped to hear some concrete evidence of how these emerging platforms actually work and how the resulting data is specifically analyzed and applied to client solutions.

Amelie.ai

With a tag line of “powering the future of mental health” the team at Amelie.ai are hoping to have a positive impact in helping to reduce suicide rates. Unfortunately, judging by the way some key statistics are presented on their home page, the data (and the methodology) are not as clear as the core message.

Using technology to help scale the provision of mental health and well-being services, combined with mixed delivery methods, the solution aims to offer continuity of care. Picking up on user dialogue and providing some semi-automated and curated intervention, the presentation was big on phrases like “triage packages”, “customer journey”, “technical architecture”, “chatbots” and of course, “AI” itself, but I would have like a bit more explanation on how it worked.

I understand that the platform is designed to integrate with third-party providers, but how does this happen in practice?

Only when asked by the judges about their competitive advantage (as there are similar tools out there – see Limbr from a previous pitch night) did the presenters refer to their proprietary language models, developed with and based on user trials. This provides  a structured taxonomy, which is currently English-only, but it can be translated.

There were also questions about data privacy (not fully explained?) and sales channels – which may include workplace EAPs and health insurers.

Businest

According to the founder, “dashboards and KPIs only diagnose pain, Businest fixes it“. In short, this is intelligence business analysis for SMEs.

With a focus on tracking working capital and cashflow, as far as I can tell, Businest applies some AI on top of existing third-party accounting software. It identifies key metrics for a specific business, then provides coaching and videos to change business behaviour and improve financial performance. There is a patent pending in the US for the underlying algorithm, which prioritizes the KPIs.

Again, I was not totally clear how the desired results are achieved. For example, are SMEs benchmarked against their peers (e.g., by size/industry/geography/maturity/risk profile)? Do clients know what incremental benefits they should be able to generate over a given time period? How does the financial spreadsheet analysis assist with improving structural or operational efficiencies that are outside the realm of financial accounting?

Available under a freemium SaaS model, Businest is sold direct and via accountants and bookkeepers. A key to success will be how fast the product can scale – via partnering and its integration with Xero, MYOB and QuickBooks.

AiHello

I must admit, I was initially curious, and then totally bemused, by this pitch. It started by asking some major philosophical and existentialist questions:

Q: How do we define “intelligence”?
Q: Are we alone? Or not alone?

No, this is not IBM’s Watson trained on the works of John-Paul Sartre (cf. Dark Star and the struggle with Cartesian Logic). Instead, it is an analytical and predictive app for Amazon sellers. It claims to know what products will sell, where and when. And with trading volumes worth $2.5m of goods per month, it must be doing something right. Serving Amazon sellers in the US and India (and Australia, once Amazon goes live here), AiHello charges fees based on fixed licences and transaction values. The apparent benefits to retailers are speed and savings.

Asked where the trading data is coming from, the presenter referred to existing trading platform APIs, and “big data and deep learning”. It also uses Amazon product IDs to make specific predictions – currently delivering 60% accuracy, but aiming for 90%. According to the founder, “Amazon focuses on buyers, we focus on sellers”. (Compare this, perhaps, to the approach by Etsy.)

C-SIGHT

A new service from the team at Pax Republic, this latest iteration is designed to avoid some of the policy and reputation issues involved with managing, supporting and protecting whistleblowers. Understanding that whistleblowers can pose an internal threat to brand value, and present a significant human risk, C-SIGHT provides a psychologically safe environment for the Board, C-suite and workforce alike, and can act as an early warning system before problems get out of hand.

Sold under a SaaS model, C-SIGHT analyses text-based and anonymous dialogue, with “real-time data sent to different AI apps”. I understood that C-SIGHT combines human and robot facilitation, while preserving anonymity, and also deploys natural language processing – but I didn’t fully understand how.

In one client use case, with the College of Surgeons, there were 1,000 “contributions” – again, it was not clear to me how this input was generated, captured, processed or analysed. Client pricing is based on the number of invitations sent and the number of these “contributions” – what the presenter referred to as an “instance” model (presumably he meant instance-based learning?).

Asked about privacy, C-SIGHT de-identifies contributions (to what degree was not clear), and operates outside the firewall. There was also a question from the judges about the use and analysis of idiom and the vernacular – I don’t believe this addressed in much detail, although the presenter did suggest that the platform could be used as a way to drive “citizen engagement”.

Overall, I was rather underwhelmed by these presentations, although each of them revealed a kernel of a good idea – while in the case of AiHello (which was the winner on the night), sales traction is very promising; and in the case of Businest, industry recognition, especially in the US, has opened up some key opportunities.

Next week: Bitcoin – to fork or not to fork?

ANZ’s new CEO on #FinTech, CX and #digital disruption – 10 Key Takeaways

I went to the recent Q&A with the new CEO of ANZ, Shayne Elliott, organised by FinTech Melbourne. It was the first public speaking appearance by Shayne since becoming CEO (excluding his gig at the Australian Tennis Open), and followed a similar event last year with Patrick Maes, the bank’s CTO.

600_446693337The key themes were:

  1. Improving the customer experience (CX) is paramount
  2. Maintaining the high level of trust customers place in their banks is key
  3. Being aware of FinTech disruption is important, but remaining focused on core strategy is even more important
  4. FinTech can coexist with traditional banks, but the latter will win out in the end
  5. The bigger opportunity for FinTech is probably in SME solutions, rather than B2C
  6. Increased process automation is in support of CX, not about reducing headcount
  7. Big data and customer analytics are all very well, but have to drive CX outcomes
  8. Customers still see the relationship with their main financial institution in terms of basic transaction accounts, which is why payment solutions (a high volume/low margin activity) are vital to the banks’ sustainability
  9. ANZ is about to appoint a head of digital banking who will report direct to the CEO
  10. ANZ has been rated as one of the top global banks in terms of its use of Twitter and social media (but from what I have seen, much of the Big 4 banks’ social media presence can be attributed to their sports sponsorship…)

There was also some discussion around ANZ’s Asian strategy, and the statement last year that the “new” strategy is about becoming a digital bank. Shayne was quick to point out that they are not abandoning the Asian strategy (it’s not either/or) but because they embarked on Asia 8 years ago, most of the work has been done. Now they need to consolidate and expand the platform they have built. He also placed ANZ’s Australian business as being a comparatively small part of the group’s portfolio, and also took the view that despite ANZ’s size, resources and reach, digital products have to be developed market by market – it’s not a one size fits all approach. (Several FinTech founders in the audience took a very different perspective on this.)

And, in a bid to appear entirely approachable, both Shayne and Patrick were happy for people to contact them direct by e-mail… So if any budding FinTech founders have an idea to pitch to a major bank, you know who to contact.

Next week: Making the most of the moment…