Fintechs can analyze startups, trade stocks at high frequency, make
lending decisions for loan applicants and move money from your paycheck
to your savings account. Equipped with big data and connected to
almost 7 billion information collecting portals, innovative startups
have transformed the finance industry. Using algorithms to discover
patterns and generate insights from the data they are exposed to,
self-learning machines can make split second decisions at ultra high
volumes, performing tasks that previously required an army of briefcase
yielding, high paid soldiers. Research and development
initiatives have transformed the finance industry into a high tech,
automated, computer driven, and artificially intelligent (AI)
sector. When engineers and computer scientists develop artificial
intelligence solutions for the fintech industry, they may be eligible
for Research and Development Tax Credits which are available to
stimulate innovation.
The Research &
Development Tax Credit
Enacted in 1981, the federal Research and
Development (R&D) Tax Credit allows a credit of up to 13% of
eligible spending for new and improved products and processes.
Qualified research must meet the following four criteria:
New or improved
products, processes, or software
Technological in nature
Elimination of
uncertainty
Process of
experimentation
Eligible costs include employee wages, cost of
supplies, cost of testing, contract research expenses, and costs
associated with developing a patent. On December 18, 2015,
President Obama signed the bill making the R&D Tax Credit
permanent. Beginning in 2016, the R&D credit can be used to offset
Alternative Minimum tax and startup businesses can utilize the credit
against $250,000 per year in payroll taxes.
Artificial Intelligence in
Lending
The key to effective artificial intelligence is
data. As data becomes increasingly accessible, it fuels momentum
in the artificial intelligence sector. Small business lenders use
new data sets to make more informed credit rating and lending
decisions. Some innovative lenders use it as a business
development tool as well. They partner with accounting software
platforms who feed them data, which they then use to identify business
owners who are in need of capital. The benefit is twofold;
lenders utilize the data not only to identify and target borrowers, but
also to assess their ability to repay loans.
There are other benefits as well. Computers
can analyze data in an instant which in turn allows lenders to make
split-second decisions without a face-to face meeting. In other words,
lenders benefit by saving on operating expenses, while business owners
gain access to same-day capital.
This innovative new way of financing is not just
limited to small businesses either. The terms of one’s next
personal loan might depend less on credit score and more on habitual
data collected by one’s smartphone. Digital lenders don’t just
analyze an applicant’s paycheck and payment history. Better
indicators of credit worthiness may perhaps be found in SAT scores,
spending habits, level of education and even response rates to text
messages. But how much of this data is actually relevant?
Currently, many startups are in the stage of analyzing patterns in
order to provide answers to that very question – or at least the
machines are. Self-teaching algorithms guide self-learning
machines towards not only the data that’s worth collecting, but
appropriate inferences that should be drawn from it as
well.
Digital lending is quite the phenomenon. Currently,
there are over 2,000 digital lending startups across the U.S. and
Europe, most of which use some level of artificial intelligence.
Fundera Inc., located in NYC, provides a marketplace for small business
loans. It works with a group of handpicked and rigorously
screened vendors, giving borrowers a place to compare, shop and apply.
Boston, Massachusetts’s-based Underwite.ai does just
as the name implies. Founded in 2015, by Marc Stein, the startup makes
lending decisions using predictive models of machine learning which are
generated by a computer algorithm, as opposed to determinations made by
statisticians based on their interpretation of linear regression
data. By applying advances in machine learning, and analyzing
thousands of data points, Underwite.ai claims they are able to
radically outperform traditional scorecards in both consumer and small
business lending.
Artificial Intelligence in
Consumer Finances
Not quite wealth management or portfolio analysis,
one sector built using artificial intelligence involves applying
machine learning techniques to consumers’ day-to-day spending and
saving habits. These digital assistants often have names like Olivia or
Erica. They help customers make smarter investment decisions, put away
money into savings or retirement accounts and alert users when they are
nearing their budget limits. Linked to the cloud, personal bank
accounts and credit cards, they can track spending habits and provide
updates in real time about a user’s personal financial situation.
Some of them even make real decisions such as lowering the thermostat
or shifting money into a higher interest rate bank account.
Founded in 2013, San Francisco startup Digit Inc.
created an app that analyzes spending habits and safely sets aside
small amounts of money for users. For example, depending on a
person’s cash flow, goals and personal preferences, every 3-4 days a
random sum of money is moved over to a savings account, held at Digit
and invested to earn a premium.
Also founded in 2013, New York City startup
MoneyLion Inc. uses machine learning and big data to regularly
underwrite loans at affordable rates. Student loans, credit
cards, car loans and other personal finances are automatically assessed
and consolidated repeatedly as the market fluctuates to provide
consumers with the lowest possible market interest rates.
Debitize Inc., also based in NYC, helps consumers
spend responsibly. The company transforms credit cards into debit
cards by providing a mechanism which consumers use to back up every
credit card purchase with real money from their checking account to
ensure that their balance is paid in full each month.
Artificial Intelligence in
Wealth Management
Some call them robo-advisors. Financial institutions
see them as a good way to automate decision making. Discount brokerages
use them as a way to offer money management services for reduced
fees. Whatever the case, one thing is clear: artificial
intelligence has significantly disrupted the way financial funds and
portfolios are managed. In 2014, automated financial advisors
were estimated to have at least $14 billion in global assets under
management. That figure is expected to grow substantially.
According to projections from Business Insider Intelligence, 10% of all
global assets under management will be automated by 2020. That
equates to roughly $8.1 billion as demonstrated in the chart below.
Source: BI
Intelligence
The range of wealth management services offered by
automated technology varies from company to company but generally
includes investment advising, basic advice, account aggregation, risk
assessment, financial planning, re-balancing and even tax
optimization. Ultimately, the advent of the robo-advisors means
empowerment for the basic investor who, instead of turning to
traditional financial advisors, will turn to an algorithm warehouse
that will generate an appropriate investment strategy based on inputs
from the user.
Traditionally, wealth management services were
reserved for the wealthy with minimum investment requirements ranging
from $100,000 to $250,000. Artificial intelligence capabilities,
however, have provided access to portfolio investment services with
increasingly low minimum balance requirements. Schwab, the
nationally known discount brokerage firm in San Francisco, California
requires a mere $5,000 for access to its automated “intelligent
portfolios”. Some startups are even more competitive. Stash
Investments LLC, a NYC-based startup and SEC registered investment
advisor, recently developed a mobile app that allows users to choose
from over 30 different investment options with a shockingly low minimal
investment of only $5. Users can even buy fractional shares in
publicly traded companies, allowing them to invest whatever they can
afford.
Artificial Intelligence in
Venture Capital
Given the hype around AI, one would expect venture
capitalists (VCs) to jump on the bandwagon. Indeed, they are. VCs
use machine learning to analyze startups, mine for data and predict
technology trends. One company even developed a fully automated
computer analyst dubbed “The AI VC.” When human venture capitalists
depart for their summer season breaks, the “humanoid” fills the void,
asking questions, collecting data and scheduling pitches for review by
human colleagues.
Other fintech companies use a more subtle form of
AI. CB Insights in New York City uses artificial intelligence to
mine data for venture capitalists in order to predict technology trends
and identify investment opportunities. Thinknum, Inc. also based
in NYC, monitors information on companies and markets to facilitate
data driven investment. Users include venture capital firms,
hedge funds and investment firms. They use Thinknum's
intelligence driven tools for research and data analysis which enables
them to generate unique, intelligent insights.
Conclusion
Artificial intelligence is generating a buzz in the
fintech industry. Driven by data, computers use algorithms to
discover patterns, generate insights and learn from their
environments. Research and Development tax credits are available
for financial technology firms who are developing and integrating this
technology.
