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

 

The NAB SME Hackathon

The recent week-long Intersekt fintech festival kicked off with a 48-hour hackathon, sponsored by NAB, hosted by Stone & Chalk and York Butter Factory, and designed to meet the needs of NAB’s SME customers.

Using NAB’s own transaction data APIs, participants were asked to come up with a solution to one of the following challenges:

1. How to make the lives of SME owners easier
2. How to help SMEs generate more business

12 teams competed over the weekend, and each presented their ideas to a panel of industry experts. Clearly, these were not the usual startup pitches (and none have a public website), but it was interesting to see the results. Projects are listed here in the order they presented:

NABTax – “tax audit insurance”
Designed to encourage better/best practice tax governance among SMEs, it uses a combination of a tax risk rating linked to a reduced cost of premiums for tax audit insurance.
The solution would help SMEs to be better prepared for an ATO request for information, aid understanding of the ATO’s current small business benchmarks, and provide insights on the ATO’s data matching protocols.
Essentially it would generate a risk rating based on quantitative and qualitative analysis of supporting documents supplied by the SME.

EasyPay – “reconciling invoices and receipts”
Deploying an e-invoicing model, the platform would generate a unique reference number, linked to an ABN, and generate a QR code to be scanned by the payer.
At its heart, it would better match invoices and payments. The service would be sold under a freemium model, and would be compliant with the New Payment Platform (NPP).
The main challenge would be in reaching and gaining traction with consumers (the bill payers).

ORDR – “managing cash-flow, inventory ordering and sales”
Drawing on a dashboard showing SKUs of items in stock, it would use machine learning
to predict stock ordering requirements. Although this concept was based on actual SME experience, the panel felt that there would be integration issues with existing POS and supply chain systems. Also, how would it link to CRM data, and how would it be able to both accommodate new season stock, and accurately forecast demand?
Finally, what level of SKU data is actually available from NAB transaction data?

Just-In-Time MBA – “a financial/business coaching app for SME owners”
According to data presented by the team, 60% of SMEs fail within their first three years. And given there are something like two million micro-businesses in Australia, and 250,000 new ones established each year, if nothing else, there is a huge opportunity to reduce this failure rate.
Using the available APIs (plus data from the SMEs’ accounting systems), the platform would analyze payments data and issue alerts designed to prompt remedial action.
Based on the presentation, it seemed that the proposed analysis is only capturing cash-flow – clearly, the real value and insights would come from holistic health checks.

NAB SME Connect – “connecting small business to customers”
Using a number of data inputs, this service would push deals in real-time to your smart phone. The customer app shows only relevant offers – based on preferences, proximity, etc. The client SMEs can see the level of interest and demand, to generate “Smart Deals” based on transaction data. The panel wondered about the opt-in model, and also felt there were already similar competitor products, or that any competitive advantage would be difficult to defend.

Wait< – “wait less for elective surgery”
Aimed at time-poor SME owners, the team wanted us to think of this as an “eBay plus Afterpay for elective surgery”. Taking the approach of a two-sided marketplace, it would
support transactional loans to cover the cost of surgery, and match customers (patients) to suppliers (health care providers). Drawing on NAB’s current healthcare payment services, the solution would combine NAB’s transaction banking and health APIs, plus Medicare APIs (for patient and practitioner verification), to generate a pre-populated lending form. No doubt designed to appeal to NAB Health, this was a very niche project.

Tap & Go – “turning customer loyalty into rewards more easily and more cheaply”
This idea would enable SMEs to use transaction data to decide who gets a discount, and how much. Built on a merchant administration platform, it would capture transaction data from POS systems. It would be offered as a subscription service for merchants. The panel wondered how this solution compared to the competition, such as Rewardle.

TAP – “smarter marketing solutions”
Commenting that only 16% of SMEs are maximizing their online presence, this service is designed to increase merchants’ digital presence. It would use NAB APIs to manage and track campaigns – by comparing the data to past sales periods and previous campaigns. Campaigns would also be linked to social media accounts. The panel questioned how the solution would fare against competitors such as Hootsuite.

StopOne – “integrated hub for making data driven decisions and connect with a NAB banker”
Conceptually, this was a very ambitious project, designed to let SMEs use dashboards and forecasting from NAB transaction data (and other sources), to drill down into visualized data records. It would also integrate with social media insights, incorporate a messaging platform to allow SMEs to communicate with their bankers, and enable SMEs to share their dashboard with a business banker. The panel queried the cost of the data analytics for the SME, which presumably comes on top of their existing accounting software.
They also suggested the team take a look at what 9 Spokes is already doing in this space.

Spike – “accounts payable solution”
Currently, paying invoices can involve a 10 step process. The average SME has 90 suppliers. Accessed via a NAB accounts payable login, the solution incorporates the Google vision API to capture an image of the invoice and extract key data points. The SME then chooses the date and account for payment, the invoice is stored in the cloud, from where is posted to the Xero ledger, and the NAB payments portal. In addition, the client can share purchase order data with their supplier to pre-populate the invoice. It could
also optimize expenses, by recommending offers or product switches. When asked about the commercial model, the team suggested it could be offered free by NAB, who get access to extra data.

nablets – “focus on things that matter”
According to this team, 90% of SMEs are not taking full advantage of digital tools. Using NAB APIs and event-based triggers, clients would use their NAB Business Connect account login to create “if this then that” rules and tasks. It would also leverage open banking data APIs. The panel asked about the logic and the parameters to be embedded in the rules-based activities, as well as the proposed categories and range of functions to be automated. They also wondered how it would actually help SMEs to adopt digital tools – some of which are already integrated into the current banking portal.

NAB Hub – “Small Business Hub”
Designed to present banking data the way customer wants to see it (P&L, balance sheet, net asset position etc.), it would also help in generating leads for pre-approved loan products, and help with investments via optimized rates, and for insurance cover it would
assist with policy reviews, claims and risk analysis. The panel asked if this was intended to be a NAB add-on or a standalone product. They also suggested the team look at what Tyro is doing around lending analysis – but recognized that there was possibly a place for this type of tailored advice.

Based on the judging, the winners and runners-up were:

1. Just-in-time MBA
2. Spike
3. NABTax

Meanwhile, the crowd favourite was Just-in-time MBA, and the best innovative idea was TAP.

If I had to summarise the presentations, it would be as follows:

1. Most of the presentations were still talking about yesterday’s/today’s banking products, rather than products of the future
2. There was very little evidence of projects designed to help SMEs grow their business
3. Any effort to gain traction for these projects will revolve around changing customer (and bank) behaviours….

Next week: VCs battle it out in the reverse pitch night

 

 

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?