DigEnd ranked the presentations of five fintechs that reached the finals of an annual incubation program run by Accenture.
Eight startups reached the final, but DigEnd has already covered three: BlueOnion, Evident and Planto. Here are links to the founders of each company discussed on our video podcast, DigFin VOX:
Okay, and then there were five. Based solely on their brief arguments, here’s how DigEnd sees the potential of its business model, with a few questions that potential investors or partners should keep in mind.
5. Oxford Risk (UK)
This spinout from the University of Oxford has developed software to help financial institutions understand how people make financial decisions. Better decisions can allow retail investors to grow their assets by being exposed to the right products, instead of remaining too heavily exposed to cash. The company calls this vision “behavioral alpha.”
The startup was born in the wake of the 2008 financial crisis, with the idea that wealth managers (banks, financial advisors) struggle to understand clients, while retail investors are intimidated by jargon. A person’s ethnicity, gender or other characteristics are less important than their understanding and appetite for risk, and their comfort with such issues.
The company says it is now integrated into the technology stacks of several financial institutions across 15 markets.
DigEndThe interpretation of this endeavor is that it is based on psychometrics, a term that is not mentioned in the speech. The company’s methodology for obtaining information is questionnaires. Such tools have been around for a long time and are used by banks to assess a borrower’s creditworthiness. They can be useful additions, but do not provide a complete client assessment on their own. This is the first time DigFin has seen these tools applied to retail investing, but the principles are likely the same, as are the limitations – particularly the 25-question surveys.
4.Bilby (Hong Kong)
Bilby’s bots constantly scan the internet for government announcements and related news. It then applies predictive models so that its clients are informed quickly about what governments might do. His pitch cited a bank’s prescient warning to its clients about China’s sudden decision to crack down on its education technology sector. This information could be analyzed systematically.
Bilby’s founding team is made up of capital markets veterans and therefore understands the value of bringing its information into the offices of investors and quantitative traders. The key to making it useful is to provide lots of structured data, but that alone doesn’t add enough value: that’s the API in trading desks. They can receive signals about upcoming or real-time policy and regulatory changes and then build business models on top of them.
JP Morgan tests it. The team is based in Hong Kong but the goal is to go global.
DigEndThe point of the pitch is that this is indeed helpful, but how big is the company’s competitive gap? Hedge funds have been buying or creating sentiment analysis software for years. Bilby may have found a new niche, but could a more established company simply add their idea to an existing sequel? The maxim that “the proof is in the pudding” applies more than ever to this service, as Bilby’s success will depend on traders’ ability to attribute alpha to his insight.
3. Libertify (France)
This company transforms documents and data into virtual storytelling: think of deepfakes, only legal and legitimate. It uses AI to provide financial institutions with a video experience: “video is eating the world” is the company’s unofficial slogan. More and more clients and counterparties prefer video over playback, so market participants need tools to communicate in this way, while using AI-generated avatars to deliver content.
Examples include relationship managers who present to clients the week’s market performance and what’s coming up; or rapid time to market. Avatars are reliable (i.e. compliant) and are now of high quality. They are meant to be engaging: the AI is meant to scan a PDF and create a video that delivers key information in a quick, user-friendly way. There is also video chatbot capability to answer questions, a useful way to extract more data on customer sentiments and concerns.
It’s more active than just emailing them information and FAQs, and if the technology can generate two-way dialogue, there’s a better chance of nudging the customer in a certain direction.
Libertify is now deploying experiments with Société Générale for its products listed in Singapore.
DigEnd recognizes that this sort of thing is going to become commonplace, but that it’s also harder than it seems, and so Libertify can gain competitiveness by pushing the boundaries. Our question is about how people react to AI-only interactions: will they truly engage or will they tune out? These LLMs can quickly become boring, and there is a risk that everyone will end up drowning in AI-generated mud. The answer is to use this tool, like any other tool, judiciously, but startups must evolve, which involves a contradiction that they and their customers will have to deal with.
2. zkMe (Hong Kong)
A new day, a new acronym: DAD, for decentralized autonomous data. Everyone in financial services wants structured (machine-readable) data, but that data is vulnerable to data sovereignty laws and other frictions around sharing and using it. This is particularly problematic in the emerging world of decentralized finance, which is supposed to be global and accessible to everyone.
The implication of self-sovereign data is that identity becomes interoperable and businesses or individuals no longer have to worry about being responsible for third-party data. This removes the justification for data brokers.
Enabling digital identities to travel seamlessly across markets and protocols will unlock many business ideas for blockchain-based businesses. For example, if people or businesses have their own trusted credentials, they can access structured data from government applications, private wallets, or a commercial bank – without needing to trust a security agent. AI or a broker, and without consolidating the vital information of each into one. database that could be hacked.
ZkMe wants to be the universal identity layer, like a virtual biometric card using mathematical proofs that your identity has been verified without having to show the actual data. This can eliminate data due diligence and liability costs, while complying with national anti-money laundering and data protection laws. And data is only shared when necessary, rather than remaining on third-party servers.
Although zkMe’s solution is now available in crypto markets, the startup is talking to commercial banks and telecom operators.
DigEndThe View: Well, data sovereignty has been a dream, and a graveyard, for many startups. It’s also a busy space, with many other teams working to produce a viable solution. DigEnd does not have the knowledge to compare one team’s technology to that of another. But technology has progressed, as have needs. Web3 is all beautiful and great, but the real use case is that we desperately need tools to restore trust in this new era of deepfakes and scary AI frauds. Solutions such as those from zkMe could be part of this package. If zkMe succeeds with banking and telecom PoCs, either integrating them into blockchain commerce or using zero-knowledge proofs for existing businesses, there is reason to be optimistic about this startup.
1. Otonomi (United States)
Usage-based data first gained traction in the retail world: consider ZhongAn digitizing flight delay insurance in China.
Otonomi introduces the concept to the larger and more complex world of ocean and air cargo insurance. Every year, some $24 trillion worth of goods move by sea and air, but goods are often delayed – and delays are getting longer, due to factors ranging from droughts to congested ports and canals to rockets Houthis. These delays strand 460 million containers at sea each year for a week or more.
Although businesses can insure their products on the move against damage or loss, they cannot benefit from delay insurance. Shippers are running out of resources. Otonomi estimates that this represents a protection gap of $50 billion.
It now introduces delay insurance based on a single measurement: transit time. If it exceeds six days by sea or three hours by air, the company will pay, no questions asked or filed a claim. It does this by leveraging cargo tracking sensors that integrate data from logistics companies via APIs, to enable data-driven forecasting and contract pricing; plus blockchain to process claims. Otonomi practices as a licensed insurance agent.
The startup now works with global insurance brokers such as Marsh, as well as freight specialists, logistics companies and forwarders. The most important implications, beyond insuring freight delays, are to help improve carrier underwriting and risk management, mitigate losses due to climate risk, and use parametric measures to reduce fraud.
It wants to expand into Asia and has established a beachhead in Hong Kong.
DigEndThe question is obvious: if it’s a good business deal, why hasn’t anyone done it before? Surely a Lloyds name could have done that. Our second question is: how many players need to integrate with Otonomi APIs for this service to work? Examining efforts to digitize trade finance using blockchain suggests that business models that rely on a network effect are very difficult to sustain. So those are our questions from this pitch, but DigEnd found the problem to be real and significant, that TAM was somewhat credible, and that the technical aspects had been proven in other contexts. Usage-based data feeds offer many improvements to current business models, provided the data is big and the market size can scale – which is the case in Otonomi’s talk. Additionally, if the startup manages to gain traction in this market, it will be able to tackle larger shares of the P&C market.