FF18 pitch night – Melbourne semi-final

As part of the Intersekt FinTech festival, Next Money ran the Melbourne semi-final of their 2018 Future FinTech pitch competition.

Ten startups presented, in the following order, in front of a panel of judges representing different parts of the Melbourne startup ecosystem:

BASIQ

Describing itself as “the future of finance”, and quoting the trendy mantra of “Data is the new oil”, BASIQ is an API marketplace for financial data. Designed to counter-balance “the Faustian pact” of big data, social media and search, and to compensate for the information asymmetry of bank-owned data, BASIQ espouses open banking, even though it is backed by two bank-related VC funds (NAB Ventures & Reinventure – see last week’s blog). With a focus on the needs of app developers, the commercial model is based on a licensing fee per user per transaction. Leveraging the AWS security layer (presumably to maintain privacy and data integrity), the pitch also mentioned “screen scraping” – so it wasn’t clear to me whether the data is only coming from publicly available sources? Currently, the platform only connects to financial institutions in Australia and New Zealand.

Breezedocs

A participant in the FF17 Semi-Final earlier this year, Breezedocs is a robotic document processing solution. In short, it can read/scan, sort and extract relevant data from standard documents that need to be presented by customers in support of a loan application. Operating via an API, it can work with multiple document types and multiple formats: data can be structured, semi-structured, or even unstructured. The benefits for lenders and brokers are reduced loan approval times and increased conversion, with much
better CX for the loan applicant as well. The goal is to help the standard loan origination process to go paperless, and could be extended to life insurance, income protection insurance, and immigration and visa applications.

Doshii

Doshii ensures that apps and POS solutions can connect to one another, via a common POS API platform. Apparently, there are 130 different POS providers in Australia, and many merchants use multiple services. Now backed by Reinventure, Doshii has a focus on the hospitality sector. The biggest challenge is physically connecting a POS to the API, so Doshii has developed a SDK. However, so far, only five of the 130 providers have signed up.

egenda

I hope I got this right, but egenda appears to be the new product name for the WordFlow solution for board agendas and meetings. Offering an “affordable web-based solution for every meeting”, the product is currently being trialed by a number of universities. The platform can convert PDF/word files into HTML, transforming and enriching them into a single secure website.
The panel asked how egenda compares to say, Google productivity suite or IntelligenceBank. A key aspect seems to be that egenda is platform agnostic – so it doesn’t matter the source of the document (or where it needs to be published to?). A key challenge in managing board papers is that it’s like herding cats – so a single but highly functional repository would sound attractive?

HipPocket

This US-based app is looking to launch in Australia. A phone-based financial decision app linked to a user’s bank account, it is designed to help with personalised goal-setting, budgeting and financial engagement. Asked whether it can support long-term goals, the pitch referred to data that suggests an increasing number of people are effectively living from pay-day to pay-day, and have no capacity to meet even the smallest of unexpected  bills. Having attracted a grant from the Queensland government, they are currently experimenting with different customer acquisition models, but they hope to prove that with daily engagement, it is possible to build a long-term relationship.

ID Exchange

With a tag line of “privacy protection power”, ID Exchange addresses a key issue of the “consent economy” – how to control who has access to your personal data, and how much, and for what purpose. With the whole notion of “trust” being challenged by decentralised and trustless solutions such as Blockchain applications; the plethora of data connections with the growth of IoT; and the regulatory framework around KYC, AML, CTF, data protection and privacy, there is a need for harmonised solutions. Under an “OptOut/OptIn” solution (from the website, it looks like this is a partnership with digi.me?), the idea is that users take more responsibility for managing their own data. ID Exchange offers a $20 subscription service – but unfortunately, based on the pitch, it was not clear what does this actually meant or included.

Look Who’s Charging

This is a platform for analysing credit and charge card transactions, to identify anomalies and reduce disputed charges. Currently with about 7.5% market penetration (based on merchant volumes?), it can help with fraud checks and spend analysis, by combining AI, crowd-sourcing and data science. But from the pitch, it wasn’t clear where the data is coming from. Also, a key part of the problem might be the data mismatch between card acquirers (merchant services) and card issuers (banks and financial institutions). Given that the growth in credit card fraud is coming from online shopping and CNP (card not present) purchases, it would seem that a better solution is to tighten procedures around these transactions?

Plenty

Plenty describes itself as a “financial GPS”, and is designed to address the issue of poor financial awareness. Only 20% of people see a financial planner, but now with robo-advice tools, even personalised advice can be scalable. Essentially a self-directed financial planning tool, it is free for customers to create a basic financial plan and when searching for a mortgages. For a subscription fee, customers can begin to access other products and advisors, which generates commission-based fees to Plenty.

Proviso

Another of these FinTechs to have featured in this blog before, as well as competing at FF17, Proviso makes “financial data frictionless”, in particular the loan application process. With 250,000 users per month, and 150 financial institutions signed up, their success can be ascribed to the way they standardise the data and the UX. Plus, they can access more data, from more sources, quicker. And then there are the analytics they can offer their institutional clients. In the future, there will be open banking APIs, plus insights, such as the categorisation of transaction types, affordability analysis, and decision-metrics.

Trade Ledger

This is a new platform that supports SME lending based on receivables, that also reduces the effort for SMEs seeking this form of financing. Given that cashflow issues are inextricably linked to insolvency risk, Trade Ledger has developed a unique credit assessment method, and is product-type agnostic. It also aims to offer automated solutions, with an emphasis on the digital UX of products, and use machine learning to generate a predictive probability of default (PPD). Currently the biggest challenge is in the multiple variations of bank credit and lending processes and models that need to be integrated or streamlined.

Of the ten pitches on view, I have to say that none really had a “wow” factor (although if Trade Ledger can scale their PPD model, and if ID Exchange spent a bit more time on defining their key message, both could be huge products). They were mostly worthy ideas, but still defined by current banking and finance procedures. Maybe these platforms need to do more with the transactional and customer data they generate or process, to uncover more opportunities. Or think about what they could do to disrupt adjacent markets? Anyway, on the night, Proviso proved the favourite with the judges.

Next week: Conclusions from the Intersekt Festival

 

Startup Vic’s EdTech Pitch Night

EdTech or EduTech? Even Startup Vic can’t seem to decide. Whatever, this education-themed pitch night was the latest event in their highly popular monthly events, held in conjunction with Education Changemakers, and EduGrowth.

Apart from the naming convention, there is also some clarification needed around the scope and definition of “education(al) technology”. First, because it’s a very broad spectrum (does it include e-learning, e-books, MOOCS, LMS?). Second, is it more about the “delivery” than “outcomes”? Third, is it only about formal pedagogy, or does it also include discretionary, self-directed and non-curriculum learning?

And so to the pitches, in the order they presented:

Become

With the aim of “teaching kids to explore, design and navigate their future“, Become is essentially a platform for early-stage career coaching. While their app is still in development (although there is a bot in use already?), Become has been running in-person workshops and other programs to test and validate the concept. The solution uses AI and machine learning technology, but it wasn’t very clear how this will actually work – maybe there are some core profiling and preference tools, some career mapping based on proprietary algorithms, and recommendation engines drawing on the data analysis?

Using a freemium model, the full service will cost $40 per student per annum. The core audience are years 5 to 8, and part of the schools adoption strategy will focus on getting high school career advisers on-board, with additional parent advocacy.

I’ve no doubt that career advice is an important part of the syllabus, but just as important are life-long learning, resilience, adaptability, and developing self-awareness and a sense of purpose. But if nothing else, in the words of the founder, Become puts the “why” back into learning.

MoxieReader

This digital reading log is all about “inspired independent reading“. Supplementing the paper-based records widely in use, the app enables children to record their reading activity, and helps teachers to assess pupils’ reading progress, based on the titles and numbers of books read, and their associated word counts and vocabulary. (In future, the app may deliver content and instructional aids.)

Using a machine learning algorithm (“like a fitness tracker”), the app can set reading challenges, and measure reading growth. Tests may be another add-on, but from what I can see, the app does not test for comprehension or context-based reading and interpretation skills. (After all “reasoning” is the 4th “R” of education – along with reading, writing and arithmetic.)

Currently launching with an ambitious social media and outreach campaign, MoxieReader already has paid sign ups from teachers, many of whom are paying with their personal credit card, and is enjoying a 30% conversion rate, and 30% referral business.

Priced at $7 for teachers per class per month, plus $100 per school/building per month (individual teachers who already subscribed will get a rebate), there is also an opt-in donation model for parents to recycle used books.

Cogniss

This is a development platform and market place for education apps. Built on game based learning and rewards packages, it also makes use of analytics and data insights to help teachers and designers build their own products.

Having seen a demand among health and well-being users, the platform is also suited for apps designed to support behavioral change, workplace learning and social learning.

Access to the platform involves a $500 set up fee, plus $50 per month per app (plus scale rates by number of users and advanced add-ons).

The platform also supports micro-transactions, for downloaded content and apps. At present, there is no formal process for teachers to embed pedagogy into the game structure. Content vetting is also a manual process, combined with experience sharing and peer ratings – but a content certification process is in the pipeline.

Revision Village

Helping students to prepare for external exams (specifically, the IB maths) this product replaces traditional in person and in class programs, with an online resource.
Also, although revision practice largely relies on past test papers, the founders have identified a chasm between the concepts taught, and the questions asked.

Developed in response to teacher demand, this subscription-based learning resource has
translated into higher results and fewer fails.

The platform is looking to extend the curriculum beyond maths, but this will largely depend on being able to license content from the relevant examination boards and syllabus providers, such as the IB.

Access is not dependent upon being logged into a school network or intranet, as it is only a web app (with individual and site licenses).

The Revision Village website claims the product is used by “More than 32,000 IB Students and 710 IB Schools”. However, it would seem that not all of these are paid-for subscriptions, as the pitch mentioned a critical mass would be 100 schools (out of a total of 2,500 IB schools) paying $2,000 each (although this is separate to the parent market).

 

Overall, I liked the tone and format of the pitches –  the products all seemed worthy endeavours, and the founders are no doubt passionate about education and learning. But I was left feeling underwhelmed, by both the content and the tech being deployed. (I guess I needed more than just passing references to “AI, machine learning and algorithms”.) All of these products rely on significant adoption rates among schools – which are some of the hardest institutional customers to sell to – and to be successful in international markets presents a further challenge, given differences of language, content and educational systems.

In the end, even the judges found it hard to pick a winner, as there was a tie for 1st place, between Become and MoxieReader. I would probably concur, as they had the edge in terms of both individual learning outcomes, and broader educational benefits.

Next week: Copyright – Use It Or Lose It?

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?

Law and Technology – when AI meets Smart Contracts…

Among the various ‘X’-Tech start-up themes (e.g., FinTech, EdTech, MedTech, InsurTech) one of the really interesting areas is LegTech (aka LawTech), and its close cousin, RegTech. While it’s probably some time before we see a fully automated justice system, where cases are decided by AI and judgments are delivered by robots, there are signs that legal technology is finally coming into its own. Here’s a very personal perspective on law and technology:

Photo by Lonpicman via Wikimedia Commons

1. Why are lawyers often seen as technophobes or laggards, yet in the 1980s and 1990s, they were at the vanguard of new technology adoption?

In the 1970s, law firms invested in Telex and document exchange (remember DX?) to communicate and to share information peer-to-peer. Then came the first online legal research databases (Lexis and Westlaw) which later gave rise to “public access” platforms such as AustLII and its international counterparts.

Lawyers were also among the first professional service firms to invest in Word Processing (for managing and drafting precedents) and e-mail (for productivity). Digitization meant that huge print libraries of reference materials (statutes and case-law) could be reduced to a single CD-ROM. Law firms were early adopters of case, practise, document and knowledge management tools – e.g., virtual document discovery rooms, precedent banks, drafting tools.

2. But, conversely, why did the legal profession seem to adopt less-optimal technology?

The trouble with being early adopters can mean you don’t make the right choices. For example, law firms in the 80s and 90s seemed to demonstrate a preference for Lotus Notes (not Outlook), Wang Computers and WordStar (not IBM machines or MS Office Word), and DOS-based interfaces (rather than GUIs).

Some of the first CD-ROM publications for lawyers were hampered by the need to render bound volumes as exact facsimiles of the printed texts (partly so lawyers and judges could refer to the same page/paragraph in open court). There was a missed opportunity to use the technology to its full potential.

3. On the plus side, legal technology is having a significant a role to play…

…in law creation (e.g., parliamentary drafting and statute consolidation), the administration of law (delivery of justice, court room evidence platforms, live transcripts, etc.), legal practice (practice management tools) and legal education (research, teaching, assessment, accreditation). Plus, decision support systems combining rules-based logic, precedent and machine learning, especially in the application of alternative dispute resolution.

4. Where next?

In recent years, we have seen a growing number of “virtual” law firms, that use low-cost operating models to deliver custom legal advice through a mix of freelance, part-time and remote lawyers who mainly engage with their clients online.

Blockchain solutions are being designed to register and track assets for the purposes of wills and trusts, linked to crypto-currency tokens and ID management for streamlining the transfer of title. Governments and local authorities are exploring the use of distributed ledger technology to manage land title registration, vehicle and driver registration, fishing permits and the notion of “digital citizenship”.

We are seeing the use of smart contracts powered by oracles on the Ethereum blockchain to run a range of decision-making, transactional, financial, and micro-payment applications. (Although as one of my colleagues likes to quip, “smart contracts are neither smart nor legal”.)

Artificial Intelligence (AI) is being explored to “test” legal cases before they come to trial, and more knowledge management and collaboration tools will continue to lower the cost of legal advice (although I doubt we will see lawyers being totally disintermediated by robots, but their role will certainly change).

There is further opportunity to take some of the friction and costs out of the legal system to improve access to justice.

Finally, and this feels both exciting and scary, is the notion of “crowd-sourcing policy“; some governments are already experimenting with hackathons to develop policy-making models, and even the policies themselves. But this does sound like we would be moving closer and closer to government by mini-plebiscites, rather than by parliamentary democracy.

Next week: Digital currencies are the new portals