ONLINE BANK: Just how are US incumbent banks using Fintech to future-proof themselves?

It's without a doubt that the global banking industry is undergoing a digital renaissance. Digitally native neobanks are serving customers at a third of the cost of incumbent banks, leveraging modern core technology architectures to innovate faster and operate more efficiently, and earning them a significant chunk of market share. Fintech companies are building solutions around lucrative niches in the value chain. A good example of this is payments unicorn Stripe, valued at a cool $22 Billion, recently announcing it will be offering loans to online businesses to support their growth ambitions. In contrast, incumbents are subjected to the limitations of their core architectures and the resultant slow rate of change to innovate and adopt operational efficiencies necessary to retain their market share.

In the US, incumbent banks are actively investing in Fintech companies as a means to "future-proof" themselves. By "future-proof" we mean three things: (1) increasing the potential for high returns in the short-to-medium term leveraging the benefits stated above -- take Goldman Sach's investment in digital lender Better Mortgage. (2) Gain exposure to emerging sub-industries, as well as, utilize new Fintech platforms to enable rapid scaling and less expensive development of ecosystems and ancillary services -- take Wells Fargo's investment in OpenFin, who is now used to help modernize the bank's software for front-and-back-office functions. (3) Lastly, reduce spending on IT by leveraging the structures of Fintech companies such as the removal of technical debt, leveraging the economies of scale of cloud-based services, and using development tools that support automation (DevSecOps).

We recently came across CB Insights' latest Fintech trends report which notes that in 2019 YTD, US banks have participated in 24 equity deals to Fintech companies -- approximately 54% of the record 45 deals in 2018. Unsurprisingly, Goldman Sachs, Citigroup, and JP Morgan Chase were noted to be the most active US incumbent bank investors in Fintech. Since 2016, Goldman Sachs has primarily invested in Real estate and data analytics Fintech companies which compliments their current strategy, Citigroup has focused on payments & settlements and Blockchain Fintechs providing evidence of a potential Banking-as-a-service platform in the near future, and lastly JP Morgan Chase has prioritized investment in capital markets and accounting & tax Fintechs in hopes of strengthening its payments play.

For those incumbents averse to Fintech partnerships, McKinsey outlines three options for replacing the core to their next generation platform. The costliest ($100M to $500M+) and most time consuming option being a full replacement of the core with "new" traditional tech platforms. Opposite to this is the cheapest ($50M to $100M) and arguably the most value-add option of migrating the bank's core onto a "greenfield" tech stack -- essentially a modular and API-first cloud-native architecture. RBS' Bó, National Australia Bank's launch of unsecured lending solution QuickBiz, and Goldman Sach's Marcus are all examples of the greenfield approach. As noted by the Economist Intelligence Unit, the greenfield approach was considered the most sought after bank innovation strategy by 36% of the 400 banking respondents, a close second was to invest in Fintech start-ups with 31%.

We have noted it before and we will note it again, greenhouse approaches are only effective when the incumbent acknowledges digital as more of a transformation strategy than a channel -- case in point is JP Morgan Chase's failed digital bank Finn. The financial initiatives of Chinese tech companies such as Alibaba and Tencent, for example, serve as a powerful representation of how a core tech chassis serving e-commerce can translate to the physical world leveraging a digital value proposition across its front, middle and back ends. This is why we still believe to see more Fintech mergers and acquisitions beyond the current industry aggregate deal value for 2019 -- more than twice the aggregate of the same period in 2018.

sdfsdfs.PNG
46953f49-4552-415d-9e81-3981310d3483.png
66d46e25-8c86-4091-a480-4d4d9a1e8f27.jpg

CRYPTOCURRENCY: The race for Central Bank Digital Currencies is more heated than you may think

While we were away, the month of August saw some significant announcements in the realm of international cryptocurrencies -- whether it was Binance launching their stablecoin project: Venus, Walmart developing its own Blockchain, Telegram launching its Gram cryptocurrency, and Japanese e-commerce giant Rakuten unveiling its digital currency exchange, these all instantiated the notion that cryptocurrency is rapidly progressing. Additionally, the continuation of the debate amongst regulators over whether stablecoin project Libra ("Facebook's" cryptocurrency) should become the holy grail to banking the unbanked globally. However, as you may recall earlier this year France's finance minister, Bruno Le Maire, stressing that “it is out of question’’ that Libra be allowed to “become a sovereign currency. It can’t and it must not happen.”

What has happened since the launch of Libra, is the warming of Central Banks, once thought to be antagonists of cryptocurrencies, into the crypto fray, with several announcing that they are exploring or experimenting with Distributed Ledger Technology (DLT), and the prospect of central bank crypto- or digital currencies is attracting considerable attention. This is, in part, fueled by the the underlying motivation for issuance (e.g. decline in the use of cash), possible design features (e.g. 24/7 availability, anonymity) and to some extent technical experimentation (involving DLT). Similarly, concerns regarding CBDCs are focused around the fundamental impact they could have on the current financial ecosystem, ultimately questioning the role of banks in financing economic activities, and making their issuance unlikely in the short run.

The People’s Bank of China (PBoC) recently announced that it was placing the finishing touches on its very own CBDC, placing it at the forefront of 44 other central banks who are researching the issuance of a CBDC. Some examples of CBDCs already under development include: Dubai’s emCash, The Bank of Thailand’s Project Inthanon, The Bank of Lithuania’s Digital Collector Coin, The UAE Central Bank and The Saudi Arabian Monetary Authority’s Project Aber, The Marshall Island’s Sovereign (SOV), The Central Bank of Iran’s Crypto-Rial, Uruguay’s e-peso, and The Swedish Riksbank’s e-Krona. According to a report by the Bank for International Settlements (BIS) Central Banks are also increasingly collaborating with each other to carry out proof-of-concept work. Collaborations include Project Stella by the ECB and the Bank of Japan, as well as a joint project by the Bank of Canada (BoC), the Monetary Authority of Singapore (MAS) and the Bank of England (BoE).

Question is, do we really need a CBDC? To answer this it is important to note that CBDCs are predominantly digital twin of traditional printed fiat currency, and are therefore to be fully regulated by the state and not decentralized. Given this, CBDCs hold two notable benefits: (1) they alleviate the costs related to printing fiat currency, maintaining its usability and security, providing infrastructure to store and move it, and distributing it. (2) CBDCs improve overall accessibility and usability of currency, this is because printed fiat currency imposes large costs on those that wish to use it -- whether its making a cash deposit into a bank account (requiring access to ATMs or branches) or safekeeping large amounts of it (requiring a secure storage mechanism i.e. a safe). Realistically, such use cases only speak to states in which the use of printed fiat currency is high, and thus the cost benefits of introducing a digital currency are significantly high i.e. Emerging Market Economies (EMEs).

The BIS report does a great job to sum up the current state of CBDC projects, noting that "At this stage, most central banks appear to have clarified the challenges of launching a CBDC but they are not yet convinced that the benefits will outweigh the costs. Those that do see clear benefits are predominantly from EME jurisdictions. This seems to be because financial inclusion projects create a clear mandate for central bank action, and a lack of current infrastructure limits the disruption a CBDC could create while simultaneously encouraging the use of new technology." Whilst a CBDC provides users with less costs incurred to use and store paper fiat currency, it will also mean a reduction in deposits with commercial banks. In turn, the resultant competition for deposits among such banks will likely increase deposit rates, driving new innovations to encourage saving and borrowing behavior -- which is good!

6015e3a9-03e3-4645-9e70-9f381b45b961.png
25e29129-3965-478a-8795-919447c431c9.png

Source: Bank for International Settlements (Proceed with caution - a survey on central bank digital currency)

INSURTECH: Breaking down how technology seeks to transform the $5 Trillion Insurance industry

When it comes to insurance, the $5 Trillion global industry is often deemed to be a slow-moving conservative sector resistant to change. Innovation is thought to be achieved by merely repackaging existing products into flavorsome marketing wrappers with re-bundled cost structures. Breaking down the variables that impact the need for change such as changing demographics and consumer behavior, enhanced connectedness through digital mediums, the emergence of the shared economy, and shift from asset ownership into renting or fractal ownership, we see a profound effect on the sector as a whole. These variables are enabled by the likes of artificial intelligence (AI) applications, internet of things (IoT) ecosystems, and decentralized ledger technologies (DLT) which help accelerate the insurance sector to respond to new trends, the streamlining of operations, reduction in costs, creation of new revenue models and evolution in innovative products and solutions across the value chain. Let's take a look at some examples.

The core to any insurance product is the back-office process of underwriting, which is leveraging AI to extract insights from various data sources, using IoT devices as the collection mediums, and cloud infrastructure to instantaneously update data to models used to improve risk profiling and thus pricing. US-based Flyreel developed an AI-enabled underwriting system replacing the need for professional insurance inspections. It achieves this via an app on a mobile device which is used to scan a property. The image content is then run through computer vision algorithms to automatically identify items relevant to the customer's policy, enabling property owners to improve underwriting efficiency. Very cool.

The reduction in fraudulent claims losses -- estimated to run the US $80 billion per year -- and improving claim settlement efficiency are crucial areas in which technology is sought to address. Inscribe.ai is a San Francisco-based fraud documents detection platform that uses a combination of natural language processing (NLP) and computer vision to scan documents to identify fraudulent claims. In terms of improving claim settlement efficiency, State Farm is testing a permissioned DLT powered by smart contracts in auto claims subrogation -- a process by which insurers settle claims losses amongst each other -- to significantly speed up the process with immediate automatic payment disbursement as soon as liability determination is completed.

Lastly, a combination of DLTs, advanced driver-assisted systems (ADAS) or other telematics installed in consumer's vehicles to collect real-time data on driver behavior and driving patterns have been essential to create more accurate real-time dynamic risk assessments and pricing models. These include pay-as-you-drive (PAYD), pay-how-you-drive (PHYD), and on-demand just-in-time insurance pricing models, spearheaded by insurers such as Cuvva, Trōv, Metromile, Insure the box, Root Insurance. On the topic of auto insurance we would be remiss if we ignored self-driving cars. Although the argument is that such vehicles will potentially reduce insurance premiums by 85-90%, new risks such as software and hardware failure, as well as cyber attack will play a major part in formulation of new premiums. Needless to say that startups such as Avinew are already offering policies to cover semi autonomous vehicles using telematics, AI, and machine learning to help build comprehensive risk assessments and policy pricing models.

It is without a doubt that not that far in the future, we will see the emergence of decentralized autonomous insurance organisations that will leverage IoT, AI, and DLTs to enable Peer-To-Peer (P2P) insurance and eliminate the need for middle men. We will see a state of the industry in which customer engagement, policy underwriting, claim filing, inspection, claim settlement, payments are customized and fully automated. And we cannot wait.

4b3f888b-1ab7-461b-b8cd-7e2785cc67e1.png
96f54cae-2d9f-4e44-8224-5c62081a28ea.png
098c2a06-ab14-4def-83f2-77f9b5b1df3e.png
8c2727b7-a75c-4916-a6d7-a8027b59ccf9.png
1dac2616-4ae1-4359-b95d-18695b4de703.png

Source: Flyreel, Forbes (State Farm And USAA See Stark Increase In Efficiency When Testing Blockchain Subrogation), Trōv, Insure the box, Avinew

PAYMENTS: In the United Kingdom, the cashless south makes the north pay

As countries like the United Kingdom, China, India and the Nordics rapidly move towards demonetization, driven by innovative technology and enhanced policy, the social and structural implications of getting rid of cash could exacerbate economic divides within these economies. Even the US is grappling with how to deal with the evolution in payments, as certain states have banned cashless checkout at retail locations (here). Based on a recent Financial Times article, the United Kingdom represents a key example of how significant regional variations in adoption of cashless transactions could leave millions, who rely on cash, isolated, exploited, and subject to increased cash handling costs. ATM withdrawals are a strong indicator of demonetization. Given this, during the first four months of 2019 compared to the same period in 2018, cash withdrawals on average declined by 8.1% across the Southern regions of England, including 8.7% in London. By contrast, withdrawals on average declined by a mere 4.7% in the remaining regions of the United Kingdom.

Additionally, in a developed economy like the UK, the share of retail transactions in cash has fallen from 54% to 41%, and is projected to land at 10% by 2026, constituting a 81% decline. In China, the share of retail transactions in cash relative to cards (excluding all mobile payments for the moment) has fallen from 64% to 48%, and is projected to land at 42% by 2020, constituting a 32% decline. Financial services infrastructure, with bank accounts as basic entry point, remove friction involved with physical cash. Point of sale solutions provide access to digital rails, which are intermediated either by finance firms, governments, or telecoms. Access to banking allows for savings and investments as well; however, there may be regressive implications for the unbanked or groups subject to specific barriers to entry in a fully card-based world.

Notably, a 2019 independent review stated that “around 17% of the UK population – over 8 million adults – would struggle to cope in a cashless society”. This reliance on cash within the United Kingdom stems from (1) the lack of infrastructure, such as reliable and extensive mobile data coverage affecting approximately 5.3 million adults, (2) the lack of financial accessibility, including those in financial difficulty, affecting 5.4 million adults, and (3) convenience, whereby 34% of the UK population wish to have the choice of payment medium to use. We expect there to be a $200B emerging new market opportunity for “Mixed Commerce”, which we define as the intersection of the payments industry, commercial activity and mixed reality (read more here).

987879.PNG
1321321.PNG
64654654.PNG
32132132.PNG

Source: Access to Cash Review (Final Report), Autonomous NEXT (Payments Frontier & Mixed Commerce Report)

OPEN BANKING: PSD2's self-defeating requirements need addressing for true Open Banking to exist

We've noted before that the implementation of Open Banking (via the EU's Second Payment Services Directive or PSD2) may not be going according to plan. As a reminder, European legislation -- PSD2 is supposed to expose incumbent banking data via structured APIs to third party providers (TPPs) that want to build upon banking information and money movement. In theory, this lowers the stickiness of bank accounts, allows data to travel safely into aggregators and apps, and lays the groundwork for financial bots and agents that make shopping decisions.

Surely, neobanks would benefit from the ability to see and move these traditional assets. Well, maybe not. According to a new report by Fingleton and the Open Data Institute for the Open Banking Implementation Entity (OBIE), "The narrow focus of the Open Banking APIs limits their potential to drive wider competition in the financial sector, for example by helping customers shop around for better interest rates on savings accounts or cheaper mortgages." Additional examples of where the PSD2 mandate failed TPPs included (1) the lack of refund functionality, and (2) the lack of functionality for customers to pre-approve payments to a merchant -- similar to a subscription debit -- instead of a customer having to manually authorize each payment,

The remedial actions for the OBIE included (1) mandate "variable recurring payments" making it cheaper for merchants to receive customer payments, (2) revised customer consent rules to remove the need for customers to re-authenticate with each TPP through their bank every 90 days, and (3) the extension of Open Banking -- beyond current accounts -- to a more diverse range of products such as mortgages, insurance, pensions, and savings accounts. Subsequent to the report's publication, the OBIE announced the initiative to create "Premium APIs" that provide a commercial incentive for banks to address the above listed failures. Those are going to need to be some considerable incentives to get the incumbents to budge. Hopefully, if the TPPs trying to build experiences don't want to deal with incumbent infrastructure, there is always Bitcoin. 

132131.PNG

Source: ODI & Fingleton (Open Banking Report for the OBIE)

BIG TECH & CYBER SECURITY: Every cloud has a surveillance lining

Let's be honest here, before the turn of the 21st century, if a stranger asked to keep our photo in exchange for a funny caricature, or a supermarket had asked to put a microphone in our homes, or a train company had asked our whereabouts in the station, or physical education teacher had asked us for our step count and sleep data every day, we would have said no. Now days, we upload multiple photos to Russian-based FaceApp, buy Amazon Alexas, use London Underground’s free WiFi, and track our activity on Garmin watches. And still manage to sleep well at night...well some of us at least. We recently learnt that both Amazonand Google admitted to having employees listen to recordings from their smart speakers. Whilst Facebook argues that its "users have no expectation of privacy" on their posts. These big US internet companies — Amazon, Apple, Facebook, Google and Microsoft — have all, to some degree, failed to protect their users' data and establish a base level of security. Controversies about how Facebook -- who received a $5B fine by the Federal Trade Commission -- shared user data with developers such as Cambridge Analytica and foreign governments earns them the lowest marks on security and data privacy, while Apple's strong emphasis on adopting considerably better policies than its more data-hungry competitors, might earn it the highest marks among the five. Other examples worth noting can be found in a previous newsletter entry here.

Relatively, there are more sophisticated means to retrieving user data without the target always being aware that it is happening. One of these was revealed by the Royal Melbourne Institute of Technology, who used various native sensors — such as the accelerometer — found in smartphones to predict the personality traits of its user. Similarly, yet more terrifying, a recent story published in the Financial Times, noted how most internet companies are equally at risk from a mobile phone spyware suite called Pegasus -- produced and sold by Israel-based “Cyber Warfare” vendor The NSO Group. The same spyware implicated in a breach of WhatsApp earlier this year. Private agencies and governments have long used Pegasus to successfully harvest private data — such as passwords, contact information, calendar events, text messages, and live calls — from the mobile phones of targeted individuals. 

Shockingly, the story focuses on the recent evolution of the spyware to infiltrate the data residing in the cloud used by the targeted individual. Such data can contain a full history of location data, archived messages and/or photos, emails, sensitive passwords, and financial records. The way it works is rather smart as it allegedly copies the authentication keys used by services such as iCloud, Google Drive, Facebook, Box, and Dropbox, among others, from a corrupted mobile phone. The keys are what these services use to verify an individual's identity, and thus provide them with access to the data on the respective cloud server. Put simply, these keys allow for an attacker to impersonate the target's phone in order to gain access to the data stored on the cloud, bypassing 2-factor authentication and login notifications. Notably, the NSO Group denies having spyware that can hack such cloud applications, services, or infrastructure.

tumblr_pv1relxmUI1rwkrdbo1_1280.jpg

As noted in the first entry above, the world is shifting to a more digital and decentralized form of finance and commerce, whether it be Wealthfront or Betterment roboadvisors assisting you in facilitating your wealth management, or using Robinhood's mobile app to enact stock trades. The truth is that most of this data flows through the cloud services of internet companies. And so long as hacking tools like Pegasus exist, coupled with our willingness to brazenly share our data with attention platforms, such sensitive data is subject to surveillance. But don't delete your Facebook profile just yet, as "good tech companies" — such as CrowdStrike, Cylance, and SentinelOne — are coming to our aid to fight and protect us against such cloud-native surveillance tech. Earlier this month, shares in CrowdStrike — the cyber security company that uncovered Russian hackers inside the servers of the US Democratic National Committee — jumped 97% in their trading debut on the NASDAQ, valuing the California-based cyber security group at $6.8 Billion. Since then, quarterly reports indicate revenues have risen 103% year-on-year to $96.1 Million, primarily due to the growing demand for its expertise in combating malicious cyber hacks. In any case, stay vigilant, as what we deem most crucial to our privacy in everyday life is what surveillance tech seeks to exploit (Read more here).

6546541.PNG
http___com.ft.imagepublish.upp-prod-eu.s3.amazonaws.png
5465654.png
PI_01.26.cyber-00-02.png

Source: Tom Gauld (New Scientist), CitizenLab (Hide and Seek Report), Financial Times (NSO Group Technologies), Pew Research Center (Security & Surveillance Report 2015), Pew Research Center (Americans & Cybersecurity)

ARTIFICIAL INTELLIGENCE: When it comes to Automation, executives get their priorities straight

It takes two to tango, and as per a report published on The State of AI and Machine Learning suggests, 82% of technical practitioners and 94% line-of-business owners believe that both humans and machines will collaborate in the future, rather than one dominate the other. The concept of such an idealistic future should instill some comfort in those whose jobs are directly threatened by automation i.e. an average of 63% of front office employees across banking, investment, and insurance industries. However, the journey to get there will be messy. With no greater example of this than Deutsche Bank's 18,000 workforce cut, complimented by a $14.5 billion IT budget injection by 2022. As noted in a recent blog entry, Deutsche's move can be viewed as the former of two possible outcomes of automation: "(1) remove $1 billion of cost by slashing your team, or (2) make your team $1 billion more productive". Amazon is undertaking an effort towards the latter outcome by spending $700 million on up-skilling its workers.

This raises an interesting point -- automation does not directly drive the loss of jobs, the priorities of c-suite executives does. In a briefing paper by The Economist on The Advance of Automation, less than half (47%) of all executive respondents strongly agree that automation is most effective when it complements humans, not replaces them. Whilst 57% believe automation will change the skills and requirements the workforce needs. Put simply, there seems to be no true preference between the two outcomes amongst the 500+ surveyed executives. Additionally, only 18% saw automation free up employees to take on higher-level roles, and 17% saw enhanced employee engagement and experience. But, is it too early to truly lean on these statistics?

Lastly, this week saw JP Morgan roll out a new digital investment service i.e. roboadvisor called 'You Invest' via the Chase mobile app. The service will target younger clients with as little as $2,500 to invest across a mix of JPMorgan ETFs, costing 35 basis points per annum. Similarly, an ex-Coutts banker has launched a digital wealth management platform called Rosecut Technologies. Combining artificial intelligence and human advice to provide a bespoke investment solution aimed towards high-net-worth clients. What we are seeing here is more evidence of automation not being the culprit behind looming job cuts. Rather its the B2B consultants promising automation solutions to executives, the pitched cost benefit of replacing workers with algorithms, and the prioritization of lean machine-driven profits, that are the true culprits. 

213230.PNG
sdasdasdsadsa.PNG

Source: Figure Eight (The State if AI and Machine Learning Report), The Economist Intelligence Unit (The advance of automation Report)

CRYPTOCURRENCY: Deciphering the $2.26B of Blockchain venture and the $3.39B raised via token offering projects in 2019 so far

According to reports by Inwara and the Crypto Valley Association, as many as 583 token offerings were launched during the first half of 2019, raising a total of $3.39 billion, whilst traditional venture funding into Blockchain-first companies raised $2.26 billion. We think the token offering figure is inflated despite our attempts at scrubbing it, and reserve the right to revise. Quality of the data continues to decline, and several projects self-reported raises in 2019 are suspicious. If anything, our intuition is that real (rather than aspirationally self-reported) ICO funding is below the venture number. 

Let's break down the token offering figure. The $3.39B is made up of 69% Initial Coin offerings (ICOs), 21% Initial Exchange Offerings (IEOs), and 10% Security Token Offerings (STOs) -- see figure below for the distinction between them. Projects stemming from China raised the lion's share ($1.18 billion or 33.2%) of the total, helped by Hong Kong based Bitfinex's $1 billion IEO raise. The USA, trailed behind China raising $255 million or 7.6% -- supported by Algorand's $122 million. Trading and investing (including crypto exchanges) has been the vertical receiving the majority of investor attention with $1.25 billion raised, and core Blockchain projects following within $338 million.

Unsurprisingly, the rise of regulator "friendly" IEOs and financial services "friendly" STOs, has meant that the number of ICO projects have declined 74% to a mere 403 in the last year. IEOs have grown from 6000% to 123 projects, and STOs 16% to 57 projects. The growth of IEOs and STOs "emphasizes a higher degree of institutionalization of large crypto exchanges around the world as cornerstones of the global Crypto Finance infrastructure – and may also be seen as a response to established exchanges moving into crypto".

So is this enough to maintain a consistent growth trajectory for the crypto industry as a whole? Hard to say, but it seems that tokenizing securities tied to real estate, and repackaged ICOs sold via exchanges may or may not result in better capital markets infrastructure, democratization and roboadvisor-led asset allocation. And second, the crypto economy needs non-financial activity to succeed. People should be building software using the global decentralized computer of Ethereum (or R3 Corda or Dfinity or soon-to-be-launched Calibra) and paying for it using the global decentralized currency Bitcoin. More crowdfunding ain't that.

dsfdsfdf.PNG
ssdfs.PNG
dsfsdfsdf.PNG

Source: Crypto Valley & PWC (5th ICO/STO Report), Inwara (Half-Yearly Report H1 2019)

INTERNET OF THINGS & APIs: The Internet of Things wasn't really a thing

Look, we love our buzzwords as much as you do, but this one has gone on long enough. The Internet of Things (IoT) is one such buzzword that is synonymous with product design, connectivity, infrastructure, and the future. Pretty broad right? To enforce this point, IoT can be simply defined as a network of interconnected digital devices in order to exchange data. Doesn't this sound like the definition for the internet? Effectively the Internet of Things is purely a term of scale, in which the "things" are any device that can be connected to the internet. The issue of scale has resulted in a mass of tech companies -- such as GoogleAppleLGSamsung, and Huawei -- each building and protecting their own IoT solution vertical with which they compete. An example of such verticals that fall into the scope of IoT include automated temperature, lighting, and security controls for your home, or fleet tracking and driver safety controls for a logistics company. For a consumer, having multiple apps to control the functions of their home is no better than using the analogue controls IoT sought to replace. For regulators, ensuring the safety, reliability, standardization and efficiency of each solution has massively hindered the deployment of IoT across the globe.

The assumption that the future of technology relies on faster, better, newer, and more hardware is debatable. Something that big tech companies like Apple are starting to realize. Rather, the future of technology should be centered around machines working together to make magic. How this is achieved is via the gatekeepers enabling the solutions -- Application Programming Interfaces's (APIs). Essentially APIs store and dispense both data and services for hardware and software. Enabling the data source(s), the data consumer(s), and the tech manufacturer(s) the opportunity to compete within the foreign land of tech platforms (i.e., App stores and e-commerce). This generally means prices fall and economic rents go to fewer winners that have strong APIs, integrations, and a nimble balance sheet. Consumer facing services such as ZapierIFTTT, and Signalpattern form part of an emerging segment, allowing for consumers and businesses to connect devices and services together to build truly innovative solutions. Similarly, payments Fintech InstaReM launched an API-based digital B2B platform enabling companies to create their own branded credit cards. Via APIs to InstaReM's card-issuing platform, customers are said to have greater control over the creation, distribution and management of card accounts -- a Visa-supported parallel to Brex.  

For the network of interconnected digital devices in order to exchange data to succeed device manufacturers need to open their APIs, like items on a menu, and users assemble them together into the perfect meal. The level of inter-connectivity we are talking about here is, for example, when the machinery in a factory stops operating whenever a maintenance person swipes into the main floor, or your car's navigation depended on your calendar, financial well-being/budget, and personal well-being (taking scenic routes when stressed). That is truly an internet of API-enabled things. READ MORE.

iot_api_platform.png

Source: Nordic APIs (APIs power the Internet of Things)

CRYPTOCURRENCY & BLOCKCHAIN: Goldman furthers the institutionalization of Crypto whilst global economic instability furthers its benefits

The Cypto-universe is experiencing what can only be described as a storm of epic proportions. Fueled primarily by warm positively-charged air coming from the launch of the Libra project, and cool negatively-charged air from the dramatic price volatility and speculation in the market. Contrary to some testaments, the likelihood of the former impacting the latter is about as much as the correlation between the price's of Bitcoin and avocados (see here). However, the coincidence of these two developments does speak to how they both capture elements of a massive, worldwide financial transformation, all happening at a time of rising global economic instability and uncertainty.

Let’s start with the mainstream global money movements over the next decade being channeled through a mix of Blockchain-era stable-money services that operate along a centralization-to-decentralization spectrum — from JPMorgan’s JPM Coin and the new Swift Blockchain project at one end, to Facebook's Libra project and more open-standard Crypto stablecoin projects such as CENTRE’s USDC at the other. And it would be safe to assume that as these projects grow in usage and adoption, so too will the demand for Bitcoin as the digital asset hedge of choice. Emphasizing this point was the recent news that the US banking giant Goldman Sachs reportedly wants in on Blockchain now more than ever, with in-depth research going into the concept of tokenization. For the Blockchain community this is Good, for the Crypto community is this Great? According to David Solomon, Goldman Sachs will be using the Blockchain to reduce its transaction costs, and improve access to and overall efficiency of services to clients. More specifically, providing greater transparency, speed of settlement, and more resilient compliance procedures. Such a move will put Goldman in line with JP Morgan, Fidelity, and Citi who have all made huge strides in the space. This is not to discount the fact that the incumbent bank has already backed stablecoin startup Circle, and toyed with the idea of launching its own over-the-counter Crypto trading desk. Yet, Goldman has failed to reveal what exactly they’re working on, and very few are waiting on baited breath. Progress in Blockchain and decentralised ledger technology has recently been so rapid to the point where news of a major financial incumbent signing on is treated as a non-event.

The wider point merges the above with significant global economic uncertainty stemming from US-China trade tensions and the significant capital flight out of China and Hong Kong. This new round of global economic uncertainty is occurring at the same time that Cryptocurrency and Blockchains are establishing themselves as key elements of the emerging financial architecture of the world. Shortly following the financial crisis of 2008, Satoshi Nakamoto posted his/her/their white paper to a select number of online cryptography experts, also known as cypherpunks. Little did they know that such an alternative model for global finance would shift the direction of large institutions and regulators alike -- with projects like Libra playing a critical role in elevating the profile of this new model. As the global economic and political stages continue to experience massive shifts caused by the vested interests of the few, so the instability independent benefits of digital assets and Blockchain are realized. As proven by the chart below indicating a strong negative correlation between Bitcoin and the S&P500.

0_1rUkc9dR-wXhD0u8.png
0_GmupIq1eq8Rd40h_.png

FINTECH & PAYMENTS: BBVA launches a product that will ‘live’ within a third party’s platform & Uber’s new move looks to restaurants-as-a-service

Three weeks ago, we wrote a story on how Fintechs such as Square and Stripe are prime examples of digital startups that have used their enrolled bases of small merchants to cross-sell other services. Additionally, ride-hailers are starting to take note by replicating this model -- using their extensive base of both drivers and riders to build out their own ecosystems. See here for a refresher.

Turns out we could have been closer to the truth. As a new alliance between car-hailing giant Uber and digital bank BBVA seeks to leverage the potential of open banking to enhance financial service provision to Uber's Mexico-based drivers and delivery partners and their families. Essentially, the Uber application becomes the interface through which the aforementioned users can open a BBVA digital account linked to Uber's worldwide 'Driver Partner Debit Card,' allowing family members to receive instant access to earnings made by the driver, without the need of costly international money transfers. Additionally, the benefits of offering a centralized and aggregated platform to drivers and their families means the collected data can be used to offer financial benefits such as loans and insurance, as well as, non-financial benefits such as loyalty rewards, discounts, and subsidized purchases. A smart move if you ask us, especially knowing that Uber is currently incurring card processing fees of around $749 million (2017) to get paid and pay its drivers.

On another note, this last week Uber announced the launch of a dine-in option to its UberEats app – this feature lets users order food ahead of time, go to the restaurant, and then sit down inside to eat. Adding Dine-In lets Uber Eats insert itself into more food transactions, expand to restaurants that care about presentation and don’t do delivery and avoid paying drivers while earning low-overhead revenue. And now that Uber Eats does delivery, take-out and dine-in, it’d make perfect sense to offer traditional restaurant reservations through the app as well. This move pits the on-demand food app directly against OpenTable, Resy and Yelp. Similarly, instead of focusing on a single use-case of on-demand food delivery -- exposing the company to the risk of heavy competition -- appealing to a niche demographic requiring such services, Uber Eats’ strategy is to own the digital service aligned to the impatient and hungry customer.

By changing gears to offer its drivers more perks and job security through the BBVA partnership, as well as, embedding functionalities that promote customer, user, and employee experiences, it’s only a matter of time before Uber launches a fully functional financial suite allowing for users to make payments, customers to maximise profits, drivers to maximise earning potential, and the incentives across the application to cater to a wider demographic as its competitors. It's always better to be a product than a feature.

uber.png
Uber-Dine-In.png

Source: El Sol De Mexico (website), Techcrunch (Uber dine-in)

ROBOADVISORS & DIGITAL WEALTH: Artificial intelligence battles in financial markets but conquers in cryptocurrencies

It has become commonplace for users of online platforms to expect that their attention i.e. time spent using the platform, converts to loyalty -- in the form of an artificial intelligence algorithm that knows them better over time e.g. auto-populating search fields, recommending preferred clothes to wear, books to read, or food to eat. Yet, when it comes to applying such sophisticated algorithms to financial markets, why aren't such quant funds always outperforming the market?

Artificial Intelligence is most useful where the problem set is narrowly defined, i.e., it is well known what is being optimized and how, and where the fuzzy data needs the structuring at scale that AI provides. A narrowly defined problem may be – given this particular set of personal characteristics about a person, should they be allowed to borrow this particular amount of money based on prior examples. A poorly defined problem may be – predict the price of a stock tomorrow given thousands of inter-correlated data points and their price history. It all boils down to the reliance of quant investment strategies reliance on pattern recognition: models look to correlate past periods of superior returns with specific factors including value, size, volatility, yield, quality and momentum. Such approaches have several fundamental weaknesses: (1) hindsight bias — the belief that understanding the past allows the future to be predicted, (2) ergodicity -- the lack of a truly representative data sample used in the model, and (3) overfitting --  when a model tries to predict a trend in data that is too noisy i.e. too many parameters or factors. Logically, over time the anomalies that these quant strategies are relied upon to exploit should dissipate, given the swift pace at which technology, competitors, and data moves to correct such anomalies. This is not stopping the likes of augmented analyst platform Kensho (acquired by S&P Global for $550 million), crowdsourced machine learning hedge fund Numerai, and the industry-leading quantamental funds of BlackRock. There is an inherent contradiction in that the approach exploits inefficiencies, but requires market efficiency to realign prices to generate returns.

With Cryptocurrencies, the strategies are different. Native Cryptocurrencies i.e. Ether and Bitcoin, are considered unconstrained assets, with limited correlations to other assets. Additionally, the data sets and factors that need to be considered when trading Cryptocurrencies are far fewer — many of which are speculative and co-dependent, resulting in far more predictable patterns than in financial markets. Because most of Cryptocurrency trading is autonomously and algorithmically driven, patterns are more easily discernible and human trading behavior often sticks out in stark contrast to established market behavior.The issue of course is not the opportunity to profit — it’s the magnitude of such profits. Currently, Cryptocurrencies simply do not have the volume and liquidity necessary for autonomous trading strategies to be deployed in large quantums. Percentage returns for algorithmic Cryptocurrency trading may be significant, but beyond certain volumes, especially when assets under management start approaching the hundreds of millions of dollars, traders need to get far more creative and circumspect in deploying funds as the opportunities are far fewer at larger order sizes.

For now at least, AI and machine learning are still some ways away from consistently beating the financial markets, but with a bit of tweaking they may be a lot closer to beating the Cryptocurrency markets. Evidence of this is already beginning to show -- in 2018 Swiss asset manager GAM's Systematic Cantab quant fund lost 23.1 percent, as well as, Neuberger Berman is considering closing their factor investing quant fund over poor performance. All this whilst Cryptocurrency quant funds returned on average 8% over the same period. While the prospect of searching for phantom signals that eventually disappear could dissuade some people from working in finance or Cryptocurrency trading — the lure of solving tough problems coupled with the potential to dip into the $200 billion opportunity means that there will always be more than enough people who will try.

8498797.PNG
fdsfdf.PNG
ssadasd.PNG
safdasd.PNG

Source: Autonomous NEXT Keystone Deck (Augmented Commerce), PWC (2019 Crypto Hedge Fund Report)

2019 FINTECH PREDICTION: Collision of Fintech Bundles and Focus on Transformation Strategies

The economic principle of perfect information is applied to instances in which arbitrage opportunities are driven away by a market with indifferent and absolute information. This principle has led us to predict that in 2019, we will see the convergence of unicorn fintech startups like Robinhood, Acorns, Revolut, Monzo, N26, Betterment, SoFi, Lending Club and others on the same multiple financial product offering across lending, banking, payments and investments. Noting that, if most players -- including large operating businesses -- understand how to market to and serve Millennials in relation to their competitors, then customer acquisition costs are likely to rise and the digital model will become more competitive as servicing costs commoditize at a cheaper price point.

Let's take this one layer deeper. Digitization costs are falling -- fueled by open banking regulation, data democratization, and freely accessible infrastructural platforms offering data storage or marketing for nothing. This is, in part, thanks to the long tail of finance aggregators such as Plaid, Bud, and Tink who pull data across multiple capital sources, using it to build/offer consumer facing products/services like budgeting tools, wealth management nudges, and/or service provider recommendations. As a result, Fintech verticals are becoming more competitive red oceans, as both big and small players fight over shrinking profit margins driven by such transparent data and freely available technology. But this isn't new news. What's happening now is a reaction by Fintech players and financial incumbents to get bigger, shed fixed costs, and take a shot to monopolize the industry vertical. The payments industry is a great example of where consolidation is happening all at once, with FIS buying Worldpay for $35 billion and Fiserv winning First Data for $22 billion. Consolidation is taking place in other forms as well, take UK-based challenger bank Revolut -- consolidating its cost exposure per transaction by building its own payment processor called RevP, and potentially launching a fee-free trading product to target Robinhood by the end of the year.

We have already seen what happens when traditional bank-backed neobanks use apps as digital channels in an attempt to capture a younger client base through edgy and innovative user experiences tied to traditional financial product -- JP Morgan's Finn became a victim of this approach which eventually resulted in its demise. Wells Fargo's Greenhouse, RBS's Mettle,and MUFG's PurePoint could face a similar fate, should they fail to acknowledge digital as more of a transformation strategy than a channel. The financial initiatives of Chinese e-commerce giant Alibaba, for example, serve as a powerful representation of how an online e-commerce chassis can translate to the physical world leveraging a digital value proposition across its front, middle and back ends. This is why we still believe to see more Fintech mergers and acquisitions beyond the current $97.53 billion industry aggregate deal value for 2019 -- more than twice the aggregate of the same period in 2018.

398305212.png

2019 FINTECH PREDICTION: Real Autonomous Organizations Take Shape

The last 5 years have seen fundamental innovation in crowdfunding, regulatory technology, the digitization of financial services, Blockchain native organizations, and automated propaganda bots to attract human attention. 2018 brought with it sobriety and a back-to-traditional regulatory treatment of financial assets and their structures. In particular, the crypto asset movement (and its crypto-anarchist community construction) has been put into a well-understood, regulated box by most national regulators. While many interesting lego pieces exist, none of them have yet to fit together. Still, regular people have gotten a taste of both the distribution and manufacturing sides of financial mana.

At the beginning of this year we were hopeful that 2019 would re-combine these pieces to instantiate functional autonomous organizations that work in a constrained market environment and perform useful services. In order to achieve this, however, these new DAOs will need a clear corporate form, a regulatory anchor, and to focus on delivering products and services to regular people, but scaled through machine strategy. We toyed with the idea that the automation of company formation (Stripe Atlas) will combine with the outsourced human/machine assembly line (Invisible Tech) and distributed governance (Aragon) to create companies that scale frighteningly quickly.

So where are the systems that deliver most of the financial primitives without human intervention? Let's start with the fact that Facebook's digital currency Libra is far from being considered a form of decentralized finance. For starters, Libra falls on a permissioned or centralized network, meaning the governance structure consists of a fixed number of entities (29 institutions), although this is said to be only for the first 5 years from release. Nonetheless, Decentralized Finance has grown to hold over $589.9 million of value across its lending, exchange platforms, derivatives, payments, and asset management entities. A notable development comes from Maker -- the most popular decentralized protocol focusing on lending -- is considering to expand the assets it uses as collateral for its smart contracts that generate cash loans. Although Maker is only considering digital tokens such as Basic Attention Token, Ether, Golem, Augur etc. at this time, would it be crazy to think that in the near term we could see the likes of tangible assets such as land, property, and commodities in the form of security tokens included aswel?

asdasdasdasdsadas.PNG

Source: DeFi Pulse

2019 FINTECH PREDICTION: Government and Enterprise Platforming, led by AI and Mixed Reality

We have saved our favorite for last. Over the last decade, consumer tech has undergone a cycle of platform building, user aggregation, data mining, and value extraction, resulting in GAFA monopolies. Exhaustion with social media networks and big tech, and the adjacent issues of privacy and radicalization, in our view, will lead to problems building new splintered consumer attention platforms for AI, AR/VR and other new media ground up. This implies that consumer platforms based on new technologies will be much more long-tail oriented, serving niche markets with very strong fit. Communities may be passionate, but smaller.

Enterprise tech lags retail adoption by, give or take, 5 years. Similar platforming has not fully penetrated on the enterprise side -- Salesforce is not yet the AI monopoly we should all fear, and Open Banking is barely a fizzle. Therefore, we expect increasing data transparency, aggregation and monetization to occur in enterprise underwritten by venture capital investors. As an example, augmented reality adoption and economics will be driven primarily by municipalities, utilities, large industrial manufacturers, and the military. We have seen this from multiple big tech players. Earlier this year Facebook doubled down on the enterprise-centric use case for mixed reality -- announcing its Oculus device-management subscription for enterprise users. Similarly, VR has found a fruitful niche as a training platform with OssoVR teaching the next generation of surgeons, and Walmart using VR to train its retail staff. Additionally, artificial intelligence at scale are to be directed largely at the workflows and manufacturing processes of large corporates. Take South African deep learning startup DataProphet who use AI and machine vision to reduce defects and scrap in the manufacturing sector by more than 50 percent. Don't get us wrong -- consumer AI is extremely important -- but within Financial Services, the scope for this in the corporate world is even larger.

The corollary is that the pricing pressure that started in consumer Fintech -- roboadvice (150 bps to 25 bps) or in remittance (600 bps to 10 bps) -- will spill over into B2B banking, money movement, insurance, treasury management and product manufacturing. An inevitable outcome, like that in the first entry above, is pressure on profit margins as prices equilibriate. For those companies that are able to re-design operations using a digital chassis, they will be able to compete on the margin with Fintech unicorns. Those that are not should exit, or retreat into more bespoke, relationship-driven business lines. This is where we are likely to see even more M&A activity over the course of the year.

chartoftheday_4602_virtual_and_augmented_reality_software_revenue_n.jpg

CRYPTOCURRENCY & BLOCKCHAIN: An adoption & regulation deep-dive in Facebook's new digital currency Libra

First came digital gold in the form of Bitcoin in 2009, then utility tokens led by Ether in 2014 and now, the global payments world could be turned upside-down by Facebook's stablecoin, Libra. It is very difficult not to be excited over this new digital currency, and without repeating the good work done by many great resources (referenced below), we wish to touch on two aspects that are important to get your head around, namely: (1) Adoption & Scale, and (2) Regulatory acceptance.

(1) Adoption & Scale

Let's get straight to the point here. According to its whitepaper: "Libra's core mission is to enable a simple global currency and financial infrastructure that empowers billions of people". As with most digital goods and services, the issue of adoption and scale is directly correlated to the efficiencies of the onramps and off-ramps (taking deposits and making withdrawals) provided by the infrastructural layer supporting them e.g., exchanges like Coinbase or Binance for cryptocurrencies. Interestingly, Libra's whitepaper mentions the term "global currency" five times, meaning that Libra's ambitions are to skip the intermediate step of concurrently using cash and digital payments, and somehow become a primary currency used by most economies around the globe.

But, just how ambitious is Libra? In short, very! We know stablecoins are traditionally backed on a one-to-one basis by mainstream assets like the U.S. dollar e.g., USD Coin, while others are collateralized by baskets of cryptocurrencies e.g., Havven. Some of these use algorithms to maintain stable values e.g., CarbonUSD. Libra is a different beast that uses a basket of real assets -- currencies such the US Dollar, GB Pound, and Japanese Yen, as well as, government bonds -- to be backed by, in what it calls the Libra Reserve. This has profound implications on adoption in targeted unbanked-heavy economies as Libra will have to coexist with the local currency, and be supported by the existing financial on-ramps and off-ramps (Bank branches, ATMs, MPesa agents etc.). Local governments are thus likely to demand concessions before allowing Libra access to its market, such as: (1) The Libra reserve must contain assets denominated in the local currency, (2) access to facets of the transaction data to track possible money laundering cases, and/or (3) permitting the local central bank to retain control over the monetary supply necessary to implement monetary policies. Iran and North Korea are good examples of a countries whose imposed sanctions by the U.S. could hinder the adoption of the digital currency by its unbanked target market.

(2) Regulatory Acceptance

Facebook have been clever here. Firstly, the Libra Association is made up of regulated entity partners who will provide the front-end platforms (on-ramps and off-ramps). Facebook is not required to become a financial entity as a result. Secondly, Calibra is set to "have strong protections in place" to keep the reserves and private information of users safe. Bank-grade KYC / AML processes are said to form part of these protections, as well as, automated systems designed to proactively monitor activity and prevent fraudulent behaviour on user’s accounts. Lastly, Libra, supported by its Association members, could be the whipping boy of cryptocurrency – defending the ecosystem against regulators, politicians, institutions, and central banks that seek diminish its legitimacy.

Such regulatory question marks have led to the creation of a task force within the Group of Seven (G7) nations to address these. There is a major concern that Libra will severely threaten not only the economic structures of the global economy, but the political dynamics as well. France’s finance minister, Bruno Le Maire, making this explicitly clear by stating that “It is out of question’’ that Libra be allowed become a sovereign currency. The G7 currently consists of Canada, France, Germany, Italy, Japan, the U.K. and the U.S.

Keep a firm eye on the Libra scales over the coming months -- like our artwork for the week depicts -- these are exciting times.

For more detail see the following:
Basic breakdown
10 Takeaways from the announcement

13990083-ea1b-4908-b9e6-7f93c645c4cb.png
37ef6239-5d54-41df-83a8-5b8379574131 (1).png
8685aa7b-67d6-4eef-b9c2-b26dc272ff02 (1).png

Source: Libra Association (via Techcrunch), Libra (via Financial Times), Facebook Libra (via Financial Times)

ARTIFICIAL INTELLIGENCE: Follow up -- Humanity fights deepfake AI algorithms with AI algorithms

Last week, we noted the terrifying reality of how artificial intelligence (AI) can now be used by malicious actors to conduct espionage. Using sophisticated AI algorithms to trick their targets into perceiving the false as real.

Don't pack for the hills just yet. What should be comforting is that the same degree of sophistication used to create deepfakes is being used to counter them. Take Adobe -- a company renowned for their photoshop product, which is often used to edit and manipulate images using their advanced software toolkit -- who is collaborating with students from UC Berkeley to develop a method for detecting edits to images in general. Initially focussing on detecting when a face has been subtly manipulated using Adobe Photoshop’s own Face Aware Liquify tool which makes it relatively easy to modify the characteristics of someone’s eyes, nose, mouth, or entire face. As with any neural network, training to move beyond this initial use-case will take time.

Decentralized public network Hadera Hashgraph has been a prominent promoter of how Distributed Ledger Technology (DLT) can play a vital role in establishing the origins of a form of media (images, video, and sound). DLTs are really good at providing an immutable and distributed timestamping service — in which any material action (an edit) conducted to a piece of media secured by the specific DLT is recorded via a timestamp. Such a timestamp could indicate an edit made by a malicious actor.

Earlier this month, saw Microsoft remove its MS Celeb database of more than 10 million images of 100,000 faces, primarily used by Chinese surveillance companies. Initially intended for academic purposes, the concern was that this database was being used to train sophisticated AI algorithms for government surveillance, as well as, deepfake applications. 

The U.S. House is currently developing the DEEPFAKES Accountability Act -- are you ready for the acronym: Defending Each and Every Person from False Appearances by Keeping Exploitation Subject to Accountability Act -- which seeks to criminalize any synthetic media that fails to meet its requirements to brand it as such. The Act would enforce creators of synthetic media imitating a real person to disclose the media as altered or generated, using "irremovable digital watermarks, as well as textual descriptions" embedded in the metadata.

Within a financial context, there is no doubt that cyber crime takes the lion's share of most financial institutions -- in 2016, JPMorgan doubled its cybersecurity to $500 million, and Bank of America said it has an unlimited budget for combating cyber crime. As the threats of deepfakes become more prominent for financial institutions, should they ensure that, not only action against such an attack forms part of these budgets, but they should be actively investing in the solutions above in order to accelerate the development in the neural networks needed to form an effective defence against deepfake attacks. 

9c7e0e69-cbc4-4d7b-bbc2-5a990a834450.jpeg
626e6034-e678-46c3-ab30-b301b2f176d1.jpg
a080db93-245a-4776-a060-d9064b944b56.png

Source: Adobe Deepfake detection tool (via Engadget), Deepfake detection (via CBS News), DEEPFAKE Accountability Act

NEOBANKS & FINTECH: Secrecy reigns supreme as JP Morgan recruit for new digital bank, and Revolut seek beta testers for their new in-house payments processor

Neobanks, Challenger banks, Digital Banks, Fintech Banks -- the complicated taxonomy of how we classify the companies bound to these labels seems to be ever-changing. What's consistent is that Fintech is, at its best, multifaceted, difficult, iterating on a solution to cater to the largest customer demographic as possible. Access and democratization are its core values, even if it is not decentralized nor truly disruptive. Get this wrong and you are subjected to a fate similar to that of JP Morgan Chase's recently deceased neobank Finn.

In 1892, two boxers, Harry Sharpe and Frank Crosby, went head to head for 77 rounds lasting five hours and five minutes, making it the longest fight in the sport’s modern history. Like one of the boxers in the late rounds of this fight, JP Morgan is pretty beat up having lost the neobank round, but the investment bank isn't done with digital-first products just yet. Although there is very little information in circulation, JP Morgan is said to be recruiting for a secretive Fintech skunkworks project based in London. The goal is to build a completely cloud-based banking platform i.e., AWS for banking, similar to that of Starling Bank or 11:FS Foundary. The offerings are said to compete with Goldman Sach's digital bank Marcus, as well as, challenger banks Atom and Tandem. Success would mean considering digital as a transformation strategy, as opposed to a mere channel. If JP Morgan get this wrong the second time then we will continue to watch them fight a losing battle in the longest match in history.

Digital as a transformation strategy seems to be the philosophy behind Revolut's latest move to build their own payments processor. We will remind your that a payment processor is a company that handles the secure authorisation communications between the different players in the payment workflow e.g., PayPal. Revolut's processor will be called RevP, and is currently in a public beta test to work out some kinks in hopes of processing the millions of Revolut transactions which take place across the globe each day. In our recent payments report, we noted that Payment Processors can take as much as US$0.30 per transaction from the merchant. The long tail of online commerce (i.e., the many small shops on the Web and social networks like Instagram) has been trending towards renting software from horizontal platforms. This includes website development tools like SquareSpace, storefronts like Shopify, various marketing agencies, and payments solutions like Stripe. Stripe claims to generate a 50-70% reduction in ongoing costs per 1,000 annual transactions, which is particularly meaningful for small businesses. This is a juicy steak for Revolut to sink its teeth into, don't you think?

ebbacdef-9d02-46e7-b666-372349063b6c.png
2f5c8d65-dbea-4821-bc5b-6273bb4f8654.png
ca551550-c81e-4bc1-8b01-30af8b55d0c3.png

Source: JP Morgan's secret digital bank (via TechCrunch), Autonomous NEXT Analysis

NEOBANKS & FINTECH: Ride-hailing apps are becoming the Uber of Fintech

Steve Jobs defined a key distinction that stuck with many entrepreneurs -- is your company a Product or a Feature? It's bad to be a feature -- you are just one widget in someone else's platform. It's good to be a product -- you fit into many environments and use-cases. What we are observing now is that Fintech product is being transformed into a platform feature by non-Fintech players -- specifically ride-hailing apps like Uber, Lyft, and Grab. 

These ride-hailing giants have built their empires by making the burden of payments a truly seamless experience for their customers. Which is why the potential for them to expand into Fintech and financial services far outweighs the need for new forms of transportation -- autonomous human-carrying Uber drones or Lyft trains. The kicker being that their robust platforms and/or large customer bases create ripe cross-sell opportunities. 

Take Grab -- the $14 billion-valued ride-hailing giant that acquired Uber's Southeast Asia business last year. Since then, Grab has faced growing competition from Go-Jek -- its +$9 billion-valued rival who is backed by Google, JD.com, and others. Forcing Grab to earmark financial services as a core pillar of its strategy for regional dominance over Go-Jek and financial incumbents who are disadvantaged by the lack of financial services infrastructure and unified credit scoring. Since then, Grab has partnered with Mastercard to launch a prepaid card to target the unbanked, spun out its own financial arm -- Grab Financial Group, which brings group payments, rewards & loyalty, and insurance to its drivers and customers, and recently announced a co-branded credit card with Citi. 

Uber's initial foray into financial services was the launch of Uber Cash -- a digital wallet allowing credit to be added in advance via prepaid cards. Since then, the popular ride-hailing app has partnered with Venmo for payments, Finnish-Fintech Holvi for offering financial services access to its drivers, Flexible car-leasing startup Fair for car leasing, a credit card in partnership with Barclays for loyalty and promotions, and a recent hiring spree showing signs of a potential New York-based Fintech arm -- much like that of Grab's. One of the interesting outcomes from such an arm would be the potential for a native Uber bank account, which would help remove the ride-hailer's reliance on the existing banking system -- Card processing fees alone cost Uber $749 million in 2017 -- to get paid and pay its drivers. Such a move would see Uber partner with cheaper and more agile low-profile FDIC-insured banks such as Cross River, Green Dot, or Chime, rather than have its own charter or partner with larger institutional banks. This is likely, as US-based ride-hailing companies such as Uber and rival Lyft have come under scrutiny by lawmakers to consider their drivers as employees rather than "independent contractors". Both Uber and Lyft argue that such a move would be cripplingly expensive -- Quartz estimates the cost to be $508 million and $290 million respectively. Our question is, to what extent would native bank accounts offset these potential employee-related costs?

Fintechs such as Square and Stripe are prime examples of digital startups that have used their enrolled bases of small merchants to cross-sell other services. Ride-hailers are starting to take note by replicating this model -- using their extensive base of both drivers and riders to build out their own ecosystems.

5c24dea6d4beaf2aa1437b64-750.jpg
s3-news-tmp-140656-untitled_design_1_17--2x1--940.jpg
5d00fc306fc920415944a915-750.png
https___blogs-images.forbes.com_ronshevlin_files_2019_05_20190512-WhyDigitalBank2-1200x675.jpg
gallery_xlarge.jpg

Source: Grab (via Business Insider), Grab Financial (via TheDrum), Uber (via Business Insider), Uber Credit (via Techcrunch), Uber-Lyft wage concessions (via SFChronicle)

ARTIFICIAL INTELLIGENCE: Proof that we have been training AI fakes to stab us in the back

In the 1933 film Duck Soup, actor Chico Marx is famously known to have asked, "who ya gonna believe, me or your own eyes?" Fairly meaningless in the 30s, but today, it's more relevant than ever. Let us explain. We know how the ever-expanding capacities of computing power and algorithm efficiency are leading to some pretty wacky technology in the realm of computer vision. Deepfakes are one of the more terrifying outcomes of this. A deepfake can be described as a fraudulent copy of an authentic image, video, or sound clip, which is manipulated to create an erroneous interpretation of the events captures by the authentic media format. The word 'deep' typically refers to the 'deep learning' capability of the artificially intelligent algorithm trained to manifest the most realistic version of the faked media. Real-world applications being: Former US president Barack Obama saying some outlandish things, Facebook founder Mark Zuckerberg admitting to the privacy failings of the social media platform and promoting an art installation, and Speaker of the US House of Representatives Nancy Pelosi made to look incompetent and unfit for office.

Videos like these aren’t proof, of course, that deepfakes are going to destroy our notion of truth and evidence. But it does show that these concerns are not just theoretical, and that this technology — like any other — is slowly going to be adapted by malicious actors. Put another way, we usually tend to think that perception — the evidence of your senses (sight, smell, taste etc.) — provides pretty strong justification of reality. If something is seen with our own eyes, we normally tend to believe it i.e., a photograph. By comparison, third-party claims of senses — which philosophers call “testimony” — provide some justification, but sometimes not quite as much as perception i.e. a painting of a scene. In reality, we know your senses can be deceptive, but that’s less likely than other people (malicious actors) deceiving you.

What we saw last week took this to a whole new level. A potential spy has infiltrated some significant Washington-based political networks found on social network LinkedIn, using an AI-generated profile picture to fool existing members of these networks. Katie Jones was the alias used to connect with a number of policy experts, including a US senator’s aide, a deputy assistant secretary of state, and Paul Winfree, an economist currently being considered for a seat on the Federal Reserve. Although there's evidence to suggest that LinkedIn has been a hotbed for large-scale low-risk espionage by the Chinese government, this instance is unique because a generative adversarial network (GAN) -- an AI method popularized by websites like ThisPersonDoesNotExist.com -- was used to create the account's fake picture.

Here's the kicker, these GANs are trained by the mundane administrative tasks we all participate in when using the internet on a day-to-day basis. Don't believe us? Take Google’s human verification service “Captcha” – more often than not you’ve completed one of these at some point. The purpose of these go beyond proving you are not a piece of software that is unable to recognise all the shopfronts in 9 images. For instance: being asked to type out a blurry word could help Googlebooks’ search function with real text in uploaded books, or rewriting skewed numbers could help train Googlestreetview to know the numbers on houses for Googlemaps, or lastly, selecting all the images that have a car in them could train google’s self-driving car company Waymo improve its algorithm to prevent accidents.

The buck doesn't stop with Google either, human-assisted AI is explicitly the modus operandi at Amazon’s Mechanical Turk (MTurk) platform, which rewards humans for assisting with tasks beyond the capability of certain AI algorithms, such as highlighting key words in an email, or rewriting difficult to read numbers from photographs. The name Mechanical Turk stems from an 18th century "automaton" or self-playing master chess player, in fact it was a mechanical illusion using a human buried under the desk of the machine to operate the arms. Clever huh?!

Ever since the financial crisis of 2008, all activity within a regulated financial institution must meet the strict compliance and ethics standards enforced by the regulator of that jurisdiction. To imagine that a tool like LinkedIn with over 500 million members can be used by malicious actors to solicit insider information, or be used as a tool for corporate espionage, should be of grave concern to all financial institutions big and small. What's worse is that neither the actors, nor the AI behind these LinkedIn profiles can be traced and prosecuted for such illicit activity, especially when private or government institutions are able to launch thousands at a time. 

maxresdefault.jpg
54646.PNG
800.jpeg
captcha_examples.jpg
amazon-mechanical-turk-website-screenshot.png

Source: Nancy Pelosi video (via Youtube), Spy AI (via Associated Press), Google Captcha (via Aalto Blogs), Amazon MTurk