How FinTech Will Change in 2018: 10 Top FinTech Trends That You Need to Know
As the year 2017 is going to its end and 2018 kicks in, we decided to do the overall review of what is going on with FinTech and what we think would be worth tracking with in a bit longer term horizon of five to ten years. Let’s dive in to FinTech trends and financial services that we think are going to shape the industry.
Foreword and General Trends
The fifth Industrial Revolution creates a lot of chatter right now. Automation, robots or robotics and AI are the general trends in IT. And, it will cause a massive job disruption.
Source: CB Insights
When you think about industrial revolution you probably think about the steam engine, electricity, computers, Internet of Things. However, now we’re looking at the fifth wave of the Industrial Revolution which is Artificial Intelligence. A lot of the talk about AI tends to be around job displacement and it tends to be about blue collar jobs – like truckers and retail sales folks. If you add up these jobs, it’s around 18 million workers just in the USA alone based on US Bureau of Labor Statistics data.
Number of jobs at risk (source: CB Insights ‘State of Automation’ Report, 2017):
- 4.6 million – retail salespeople
- 4.3 million – cooks and servers
- 3.8 million – cleaners
- 2.4 million – movers and warehouse workers
- 1.8 million – truck drivers
- 1.2 million – construction labourers
We think the other side of AI is Expert Automation and Augmentation Software (or EAAS) and this is software that is going after the white-collar jobs – technology that replicates human cognition. It’s happening in legal, trading, HR and journalism to name a few. The jobs that historically people did not think can be augmented or potentially automated by Artificial Intelligence.
CB Insights asked who is going to be disrupted in finance by AI. Venture Capital and investment bankers with 8% each are believed to be safe with the trend. However, traders (46%) and wealth managers (38%) came up pretty high.
In the landscape of EAAS we can find segments such as traders, investment managers, reporters & editors, accountants/compliance & auditors, software developers, teaching assistants, lawyers, HR managers & career coaches, marketers, CRM & sales clerks, and research & advisory.
Software and Information Technology Developments
Software is eating the world and the great illustration is that in 2006 out of five the most valuable companies by market cap, only one was in Tech (Microsoft Inc). In 2011 still only one company was in Tech (Apple Inc). However, in 2016 all 5 of the most valuable companies were in technology space – Apple, Alphabet, Microsoft, Amazon and Facebook.
One of our most favorite visual of all time is the one about technology adoption. If you look back at the telephone in 1900, it took 105 years for the telephone to reach 90% penetration. Then at 1990 where the cellphone comes in, it took 15 years to get to the same point. That pace is accelerating even faster. With Facebook reaching over 145 million users in 4 years or WhatsApp with over 400 million users also in 4 years. One of the most interesting case is a mobile carrier in India that got to 100 million users in six months. The pace of technology adoption is really quick.
Source: Michael Felton, The New York Times, hbr.org
Hence, your decision-making has to speed up – it boils down to what market should we enter, what are our competitors doing, who are the insurgents that we need to worry about and what are the disruptive technology threats and technologies that are out there.
The Emerging FinTech Trends Disrupting Financial Services.
Why should you care? We find corporations are motivated by two things it’s either fear or greed. Some people are scared of Technology, some people see it as an opportunity. Either one is motivating so that’s why you should pay attention to these trends.
Let’s dive in to 10 trends that are going to disrupt financial services and financial market.
- Amazon the bank
- Virtual real estate moguls
- Instant mortgages
- Robot-run bank branches
- Digital identity
- Robo-regulator
- Massively multidimensional trading simulations (MMTS)
- Precision insurance
- Alternative data consensus
- Human IPOs
#1 Amazon The Bank
Amazon Prime has 66 million members so we can easily state that Amazon is on a tear. Just the rumor of Amazon entering the space will send competitor’s stock price down. They are everything store in the truest sense of the word. They are expanding everywhere. Could banking be its next target?
When you look at Amazon from a customer satisfaction perspective they do better than most of the incumbent financial services institutions. When you talk to Millennials, surveys will say they would prefer to bank or do financial services with Google, Amazon or Facebook. If they start lending money and extending credit, it will feed their buying machine that they’ve built. Their machine learning algorithms have huge potential in Fin Services.
Amazon has already great payments traction. When you look at Amazon IP registry, you can see they’re working on facial identity and selfies as a means of payment. IP is a great leading indicator for what Amazon is working on. They’ve been extending credit to small businesses and their ambition is unmatched in terms of what they’re trying to do.
They play the long game. The patent from 2004 shows extraction of bank routing number from information entered by a user. Hence, we can safely say they’ve been thinking about financial services for a long time.
#2 Virtual Real Estate Moguls
Version one of virtual real estate was domain names and the biggest sales were tremendous. With VacationRentals.com sold for 35 million USD in 2007 or PrivateJet.com for 30.18 mililion USD in 2012, it is a great proof of virtual estate importance and growth.
The virtual goods industry was worth 15 billion dollars in 2012. Pokemon Go did a billion in seven months which proves how rapidly the market is expanding and how fast is the pace of technology adoption. Also, the real exchange rate between virtual and real currency is happening. You can actually translate virtual coins into real hard money or cryptocurrency and people are making real money out of it.
Another opportunity is that the property developers are emerging in to the virtual real estate, which is kind of crazy obviously. Currently, it is the Wild West of unregulated market. For instance, eBay doesn’t allow you to sell World of Warcraft items on eBay. However, people want to do this and they find other ways of making that happen. Hence, there is a lot of virtual good marketplaces, mostly outside of the US like Itemku, Playspan or Viximo. We can assume that folks who are in the tangible real estate world are going to get into here soon. Westfield is the only one that even has a virtual reality play of any kind right now.
What does this mean? Are we going to be extending mortgages for virtual real estate? Are we going to have a counterpart to all of these companies focused on virtual real estate? If so, it might be a big opportunity for new market entrants. Even if it seems like an insane idea.
Another question that comes to mind is whether these companies might become as big as normal real estate companies? And when you look over a longer time horizon, you might notice that insurers, property owners and real estate incumbents will be impacted by this move towards virtual real estate.
#3 Instant Mortgages
The process of getting a mortgage is not enjoyable one. It does not look good at the numbers either. It takes $7000, 400 pages, 50 days and 25 humans to process and originate a mortgage. Chief Information Officer at Experion seems to find this process broken as well.
“At the end of the day, in order to get that check and get the money deposited I still have to go to a title office and sign 50 documents so the money can be wired to the property owner—customers hate that, it’s a broken process.”
There is a lot of startups who are trying to digitize the home loaning process. Rocket Mortgage is an example of the early success in terms of getting to approval decision in minutes.
The question is whether you can do the ‘Uberfication’ of mortgage lending with data analytics to get instant mortgages and reduce a lot of pain that happens today? Fannie Mae’s and Freddie Mac’s are trying to come up with uniform mortgage requirements to improve quality of loan data. Not sure though if the innovation in this market is going to come from Fannie Mae and Freddie Mac.
Additionally, there are startups like Cape, Airware or Zillow who uses alternative sources of data. The question is how can we use satellites and algorithms to do estimates of properties in real time? It might reduce a lot of the friction in the mortgage lending process today. Impact of such technological developments are especially on financial services firms and mortgage companies that are involved in the process.
#4 Robot Run Bank Branches
Amazon’s already talked about having a store without cashiers. So why is that not going to apply to banks? From an automation of repetitive tasks, being able to be open 24/7 or increasing scale of operations. How would that look like in the future? We may have drones that are doing security checks using facial recognition, we could have robots that greet you in your preferred language, robo-tellers that works 24/7 or AI-infused financial advisory tools that help you right in the branch and provide you with financial decisions.
Furthermore, it’s already happening – we’re seeing this in Japan and banks like Mitsubishi UFJ Financial Group, ATB or Mizuho are testing NAO robots for improving customer relations. Also, security robots are happening so the idea of robots in the bank is here to stay and worth to look out for.
#5 Own Your Own Identity
Currently, we have so called ‘data gatekeepers’ who supposedly are here to protect our privacy from any sort of breaches. Alphabet Inc, Amazon, Apple, Facebook and Microsoft are some of these gatekeepers and they collectively earned $25B in net profit just in Q1’17 alone. We could add to this list companies like Experian, Dun and Bradstreet or Axiom that own your data and they make a lot of money out of it.
However, the collectors of data and the creators of data sometimes have different priorities. Hence, these misaligned incentives lead to stories like these:
It is partial outcome of antiquated state-issued IDs. Although we see changes in the matter. India is leapfrogging identification by putting its citizens onto Aadhaar that uses biometric and demographic data of the resident and treat it as digital identity. But, what happens if this type of system gets compromised?
We’re thinking that decentralised self-sovereign identity that uses blockchain can come in handy. Basically, you as an individual are the administrator of your own identity. It means that you control your identity and you will give access to third parties on as-needed basis. And instead of being in a database that anybody can pull you up and look at whenever they’d like, you end up owning and administrating your own identity.
Obviously with a lot of the work that’s happening already in Blockchain, it’s going to be an interesting problem to tackle.
#6 Robo-Regulator
The robo-regulator is just really regulation catching up with technology. In the U.S. alone, the Securities and Exchange Commission (the SEC) has 4870 employees and a budget proposed for 2017 of $1.78B. So can we move to a robo-regulation model that replaces all of these people with robots? Same as it’s already happening with Wall Street. With algo-trading that is in the developments, we believe it can be used by regulation authorities. As an example, the SEC is starting to talk about taking machine learning approach to behavioral predictions for market risk assessment and potential fraud and misconduct identification.
How could the Artificial Intelligence for regulatory bodies technically work?
Firstly, they could model baseline for regular and legal behaviors and through pattern recognition, predictive analytics and natural language processing, the SEC or any other financial regulator would flag abnormal activities that might be fraudulent or problematic. These flagged activities would be checked by humans to confirm its validity and the output of such verification would come back to the feedback loop of such machine learning system. Finally, the system would be able to learn through feedback loop to reduce the number of false positive results and make better recommendations in time.
For this to happen, we need more data. We have companies like Onfido for background screening, Trooly for behavior analytics, Avasdi for compliance intelligence or Qumram for risk identification. These exemplary startups are building the foundation to the robo-regulator and if they weave together, it might become the thing.
From an incumbent perspective the cost of regulation goes down, the more automated it becomes. And if you become better at finding the bad actors, it should hopefully reduce the stress on the good actors out there.
#7 Massively Multidimensional Trading Simulations (MMTS)
When you look at simulations, a lot of them are in gaming and VR applications but now use cases appear also in trading and enterprise functions. Companies like Unity that raised $596M or Improbable that raised $554M dollars and are going to disrupt the space even more.
Monte Carlo simulation is the weapon of choice for scenario planning but there is a need to do that more rigorously.
With help comes massively multi-dimensional simulations that are able to model things like the Big Bang. It looks at massive amounts of inputs and dimensions to figure out what the world might look. MMTS introduces simulation engines to econophysics that would be able to build real one-to-one model of the economic world. When you look at the former solutions and black box hedge fund strategy, you notice that through MMTS we are shifting in to the quantopian model where you crowdsource trading algorithms and utilise coding skills to improve it. Eventually, we will face hedge fund wargames where all of them will be building MMTS to figure out what the world is going to look like based on simulations that they’ve built.
Obviously, investment market and precisely financial data providers, macroeconomic experts and hedge fund traders would be impacted by the trend.
#8 Precision Insurance
Nowadays, insurers asses risk based on population, group or mortality tables.
However, it will change drastically in coming years. One of the best example is 23andme that uses gamification and saliva collection kit to get advanced warning of impending health conditions and then selling insurance based on anticipation of that.
Thanks to the cost of genetic testing that has come down significantly (from $100M in 2001 to $200 dollars in 2017), better ability to predict mortality and gaps in genetic data protection, insurance companies would have better understanding of mental health risks, susceptibility to different diseases and then be able to build policies that are very person specific.
All of Us is yet another example of the company that do disease prediction and they set a bold goal to gather data from one million people to learn more about biomarkers that signal increased or decreased of developing certain diseases. It will take into an account person specific differences like lifestyle or environment to measure it accurately.
However, the protection of genetic data is one of those ethical quandaries. If you look at our own personal privacy, people will give up a lot of it for a better deal. When you look at genetic testing and data, it will likely become more and more open and available over time. We can already notice partnerships like MassMutual with Human Longevity Inc to offer full genome sequencing to MassMutual clients and employees. Or, Anthem, CMS and United Healthcare that partnered up with Color Genomics to offer insurance based on its Hereditary Cancer Test. These partnerships can use genome sequencing to do what they do excellently – build better insurance products. Hence, we can estimate utilization of predictive genomic data, individualized stratification models for risk, or insurance and biology intertwine in the future.
As the consequence, we might see individuals who might have been considered low risk before (affluent or people who look fit) will actually be transitioned to higher risk categories given the level of precision. And vice versa, some people who were perceived as high-risk actually aren’t.
We’ll shift from broadly defined risk pool to very individually stratified risk subpopulations and thus life and health insurers, long-term care employers and policyholders will be highly affected.
#9 The Alternative Data Census
Tons of data is being created and the US or other country’s Census will seek to control the cost of census by implementing innovations and modifying IT systems. The Census Bureau tries to pull in data from other government agencies to supply essential data about hard-to-count households and vacant homes. But there’s a bunch of data alternatives that they can start pulling in data like social media, transaction and corporate data or sensors that include geolocation and satellites. The Census is eventually going to build datasets of the alternative data landscape.
As an example, Buildzoom’s home building index models its indicators highly consistently with the census.
We noticed private data providers be able to do a lot of what the government can do. Virginia Tech Study created a framework to assess the use of external sources of data to enhance federal statistics. From 61 sources that has been discovered they whittled down 11 sources that have the potential to replace existing data. As the outcome, the census can end up cutting billions in costs and increasing the frequency at which they can do census.
#10 The Human IPOs
We left the craziest FinTech trend at the end as the idea might seem a bit controversial.
US household debt hit 266 trillion in 2016 and thus the idea of selling equity in ourselves instead of raising debt came up. It’s a concept where you sell your future earnings and productivity to the market in order to fund upfront costs like education. There is a massive overhang of student debt that hit 1.3 trillion in 2016. Furthermore, when you look at graduate data, you can have a pretty good sense for what your median lifetime earnings are going to be.
Source: Georgetown University
Therefore, we take a different model to fund education instead of simply taking debt. To point out an advantage, you could use this to increase your productivity by investing in health care and well-being which today a lot of people put off health care expenditures because of the cost associated with it. However, putting it off can hurt productivity and your long-term earnings potential.
Below you can see GoFundMe chart which shows the amount of money raised on that tagged medical purposes. There is over 350 million dollars raised on this crowdsourcing platform but these are just grants and donations.
Source: CB Insights
One gentleman, Mike Merill, sold himself for $1 a share and below you can see his share price over time.
Source: kmikeym.com
Potentially, you could have activist hedge funds that tell you how to live your life and what life choices you should make. In this imaginary scenario, it’d be good to ask what’s the role of the investor in determining your life choices?
There are already existing startups that bet on it. Fantex offers to buy equity in sports players. Align Income Share Funding offers personal loans based on percentage of your income rather than repaying it at set interest rate. As you can see it’s starting to happen in some form and we think it’s worth to track.
On top of that, we believe that a new breed of underwriters might come up. These underwriters would be assessing the risk of people in a different way than maybe insurers would. They’d be responsible to assess risk and figure out how much the equity an individual is worth.
So who would be or should be responsible for evaluating and conducting ‘the Human IPOs’ if they appear?
Afterword
The next 5 to 10 years are going to be very interesting if we take those trends into an account. As you can see, lion’s part of these trends base its value proposition on big data analytics, artificial intelligence and figuring out tons of data to come up with processed outcome.
Although, they might cost a lot of people their jobs, it is safe to say that it will simultaneously create new business and job opportunities.
Do you have any comment on it? We encourage you to leave it below!
PS. If you want to learn about tech stack that some of the most promising FinTechs use, read our e-book The Perfect FinTech Stack: Analysing the biggest FinTechs and their technology.
