In our previous segment, we did a deep dive into the market landscape of data-sharing platforms in fintech and the problems they encounter at various stages.
Here, we talk about Web3, the various trending technological innovations in the space, their inherent risks, and the potential for adoption in data-sharing platforms. Finally, we touch upon how these nascent technologies can be used to help level the playing field.
Let’s get to it.
What is Web3 and Why Does it Matter?
Before getting into any of the above, we’ll talk about what the phrase Web3 actually means.
The first iteration of the World Wide Web was (at the time) a marvel - near-instant access to information at the click of a button, the ability to read and obtain knowledge that the web had to offer, be it from educational institutions, corporate companies, news sources or free content meant for knowledge sharing.
The second created a revolution - the ability to read and write turned the whole world into a smaller, more expressive place, creating millions of ‘experts’ related to every field under the sun, through the social media boom. (See also: Is Twitter as we know it dying?)
The third generation of this phenomenon is primed to introduce another element into the mix: ownership. With the ubiquity of cloud computing, and ultra-fast internet connectivity a staple of most developed as well as developing countries today, the ability to distribute and decentralize work for collaborative projects across the globe has become easier than ever before.
However, getting sufficient buy-in from creators, developers, contributors, business stakeholders, etc., is a non-trivial execution roadblock. A high degree of motivation is required front top down to make any dream project a reality.
Technology may evolve, but the human element is still the highest-value ‘product’ a company can boast of. Providing all contributors and members in a company’s value chain a degree of ownership is potentially the best way possible to attain full alignment.
Think of how private technology companies are able to attract top talent and compete with the MAANGs of the world - through attractive equity grants, or employee stock purchase programs, where the potential to build long-term compounding wealth is a significant pull.
Even this model is not without an element of role-based distribution and not truly able to provide equal value across geographical locations.
DAOs and Their Current Landscape
The ever-changing canvas that is blockchain technology has churned out a new buzzword for the world to obsess over: The Decentralized Autonomous Organization (DAO). Of all the potential use cases for blockchain technology, this is one that has evoked interest across industries, countries and projects.
DAOs have been created to move the world economy towards a fairer compensation system proportional to the scope and size of the work put in. Every DAO member is issued Governance tokens (network tokens), a form of digital currency that is implicitly valuable within the DAO’s underlying platform network.
All regulations are created and enforced using smart contracts - in layman’s terms, self-executing contracts controlled by software algorithms.
In this vein, consortium-based projects stand to gain a lot of value from this structure. Managing incentives across consortium participants can be automated to a high degree, along with bringing a hitherto unseen degree of fairness into the process, with transparency at the core of blockchain tech.
What is the True Market Sentiment Around DAOs?
A quick look at the Gartner hype cycle for Web3 shows that DAOs are firmly in the ‘Innovation trigger’ phase of their growth.
Innovation trigger: This is the phase when a new disruptive discovery kicks off excitement across the industry or set of industries impacted by it; a race begins - to build the next big thing stemming from this discovery. DAOs today are in this phase.
Peak of expectations: Early success stories become widely known, and companies begin to adopt use cases - a great example of this is the adoption of stablecoins such as Tether for cross-border remittances
Trough of disillusionment: DeFi, or Decentralized Finance is the first thing to come to mind in the world of Web3 when we think of ‘disillusionment’. Well-publicized issues and subsequent failures of companies such as Cred and Celsius are primary reasons behind the lack of excitement among incumbents, new entrants, and the wider market in this space.
Slope of enlightenment: Wider potential of technology products becomes apparent to the industry - use of blockchain tech in supply chain applications is a great example of this - taking blockchain beyond cryptocurrencies.
Plateau of productivity: Widespread use cases that penetrate the market completely (i.e., mainstream adoption) - Web3 and crypto is too nascent to get to this point.
Blockchain Tech is In Flux: Why the World Needs a Viable Use Case
As you are reading this, you might be wondering why a risk infrastructure product like a fraud data consortium would try to find correlations with the world of blockchain, which is being hit with shockwaves on an almost daily basis. At a very high level, three reasons that the crypto industry is currently in a tough spot are as follows:
- The lack of regulatory oversight
- Regulation around cryptocurrency and the entire field is nebulous, and non-standard at the moment. Companies and individuals have many ways to find loopholes in the system
- Speculative investment and trading with a ‘get rich quick’ mindset
- ‘Decentralized’ products with almost centralized control
- This has made a small set of well-funded companies single points of failure, especially problematic because data once on the blockchain is immutable
In data sharing products like we’ve been discussing, handing full control to any one entity adds a few layers of risk in case of any legal, moral and, above all security complications.
The solution to this conundrum could be in the form of a DAO that brings power back to the participants, alleviating incentive misalignment and eliminating the risk of a single point of failure with unilateral control owned by a single service provider or vendor.
Now that we’ve covered privacy, security and participants’ inability to rely on unilateral control, we dive a little deeper into the fintech industry’s data standardization (mentioned previously) challenge.
Data Standardization in the Fintech Industry
In a bygone era, financial services revolved around banks - which in turn meant that data structures (how a bank would model a transaction, a user etc) didn’t change significantly from provider to provider - instead, the competitive landscape that was driven by regulatory constraints and any product or service differentiation came in the form of highly empathetic account and relationship management professionals on the banks’ payrolls.
In the digital-first world of today, differentiation comes primarily from feature/ service level innovation, as automation has begun to reduce the human touch to financial services.
Crucially, the underlying data itself is no longer conforming to one or a standard set of schemas - the wide variety of use cases that exist in the market have made these formats nearly unique per fintech platform.
This can make product development difficult - which is both true and valid, as well as a false - as it is a limited way of thinking about the wide-spanning potential of such products. More on that view below.
Can a Broader Problem Set Result in a Generalized Solution?
As far as market trends go - the famous a16z statement from 2019 could not be more applicable. “Every company will be a fintech company.”
Payments (in some form or another) and fintech applications are being embedded and integrated into every service that has a critical mass of users. Typically, money movement is a lucrative margin business for companies to pursue, and allows them to further target their customers with deeper data and insights into their purchasing habits and spending power.
Additionally, typical end customer benefits include loyalty points and similar further incentives to keep them engaged throughout their LTV (Lifetime Value). Think about Gap credit cards and rewards as an example.
Closer to home, for most data sharing consortiums: The wider the net they can cast (from a solutions perspective), the bigger the scale, and subsequently the higher the impact they can have in parallel or complementary markets.
Let us continue to use the example of Gap cards - does Gap necessarily care that customer XY is a loan defaulter at a fintech? No, not directly.
However, Gap would like to be able to anticipate potentially delinquent customers and so on in the era of Buy Now Pay Later, wherein the liability and risk model has flipped on its head.
With this backdrop, its reasonable to assume that data regarding delinquent fintech users is valuable to companies such as Gap (which could open up the whole retail market as one of many new use cases).
Building out deep specialization in one area of fintech or one use case will be supremely valuable to the market, but the utility of such data sharing products has a ceiling.
Generally speaking, attempting to build a data sharing platform catering to fintechs and embedded finance companies will scale better if the product is not strictly opinionated about the types and vectors of fraud being tackled, rather focused on the data itself, and providing aggregated insights that help in risk mitigation.