Machine Learning in Finance – Present and Future Applications

Emerge | Daniel Faggella | Jan 30, 2019

fintech and machine learning - Machine Learning in Finance – Present and Future ApplicationsMachine learning has had fruitful applications in finance well before the advent of mobile banking apps, proficient chatbots, or search engines. Given the high volume, accurate historical records, and quantitative nature of the finance world, few industries are better suited for artificial intelligence. There are more uses cases of machine learning in finance than ever before, a trend perpetuated by more accessible computing power and more accessible machine learning tools (such as Google’s Tensorflow).

Today, machine learning has come to play an integral role in many phases of the financial ecosystem, from approving loanscredit scores, to managing assets, to assessing risks. Yet, few technically-savvy professionals have an accurate view of just how many ways machine learning finds its way into their daily financial lives.

At Emerj, we’re fortunate enough to speak with hundreds of AI and machine learning executives and researchers in order to accumulate a more informed lay-of-the-land for current uses and applications.

See:  Using AI to Enrich the Customer Experience

In this particular article, we’ll explore in the following order:

  • Current applications of artificial intelligence in finance, banking, and insurance
  • Potential future applications of artificial intelligence in finance
  • Noteworthy companies operating at the intersection of AI and finance
  • Related Emerj executive interviews

Note that this article is intended as an executive overview rather than a granular look at all applications in this field. I’ve done my best to distill some of the most used and most promising use cases, with reference for your additional investigation.

We’ll begin by looking at present applications:

Machine Learning in Finance – Current Applications

Below are examples of machine learning being put to use actively today. Bear in mind that some of these applications leverage multiple AI approaches – not exclusively machine learning.

Portfolio Management

The term “robo-advisor” was essentially unheard-of just five years ago, but it is now commonplace in the financial landscape. The term is misleading and doesn’t involve robots at all. Rather, robo-advisors (companies such as Betterment, Wealthfront, and others) are algorithms built to calibrate a financial portfolio to the goals and risk tolerance of the user.

Users enter their goals (for example, retiring at age 65 with $250,000.00 in savings), age, income, and current financial assets. The advisor (which would more accurately be referred to as an “allocator”) then spreads investments across asset classes and financial instruments in order to reach the user’s goals.

See:  Differences Between AI and Machine Learning and Why it Matters

The system then calibrates to changes in the user’s goals and to real-time changes in the market, aiming always to find the best fit for the user’s original goals. Robo-advisors have gained significant traction with millennial consumers who don’t need a physical advisor to feel comfortable investing, and who are less able to validate the fees paid to human advisors.

Algorithmic Trading

With origins going back to the 1970’s, algorithmic trading (sometimes called “Automated Trading Systems,” which is arguably a more accurate description) involves the use of complex AI systems to make extremely fast trading decisions.

Algorithmic systems often making thousands or millions of trades in a day, hence the term “high-frequency trading” (HFT), which is considered to be a subset of algorithmic trading. Most hedge funds and financial institutions do not openly disclose their AI approaches to trading (for good reason), but it is believed that machine learning and deep learning are playing an increasingly important role in calibrating trading decisions in real time.

There some noted limitations to the exclusive use of machine learning in trading stocks, currencies (ForEx) and commodities, see this Quora thread for a good background on machine learning’s role in HFT today.

Fraud Detection

Combine more accessible computing power, internet becoming more commonly used, and an increasing amount of valuable company data being stored online, and you have a “perfect storm” for data security risk. While previous financial fraud detection systems depended heavily on complex and robust sets of rules, modern fraud detection goes beyond following a checklist of risk factors – it actively learns and calibrates to new potential (or real) security threats.

See:  Crowdfunding still thriving in AI and fintech despite risks

This is the place of machine learning in finance for fraud – but the same principles hold true for other data security problems. Using machine learning, systems can detect unique activities or behaviors (“anomalies”) and flag them for security teams. The challenge for these systems is to avoid false-positives – situations where “risks” are flagged that were never risks in the first place. Here at Emerj we’ve interviewed half a dozen fraud and security AI executives, all of whom seem convinced that given the incalculably high number of ways that security can be breached, genuinely “learning” systems will be a necessity in the five to ten years ahead.

Loan/Insurance Underwriting

Underwriting could be described as a perfect job for machine learning in finance, and indeed there is a great deal of worry in the industry that machines will replace a large swath of the underwriting positions that exist today (see page 2 of this Ernst & Young executive brief).

Especially at large companies (big banks and publicly traded insurance firms), machine learning algorithms can be trained on millions of examples of consumer data (age, job, marital status) and financial lending or insurance results, such as whether or not a person defaulted or paid back their loans on time.

The underlying trends that can be assessed with algorithms, and continuously analyzed to detect trends that might influence lending and ensuring into the future (are more and more young people in a certain state getting in car accidents? Are there increasing rates of default among a specific demographic population over the last 15 years)?

These results have a tremendous tangible yield for companies – but at present are primarily reserved for larger companies with the resources to hire data scientists and the massive volumes of past and present data to train their algorithms.

We’ve compared the AI investments of insurance giants like State Farm, Liberty Mutual, and others – in our complete article on AI insurance applications.

See:  Global Financial Innovation Network (GFIN) – Regulators Launch Global Sandbox Pilot

Future Value of Machine Learning in Finance

The applications below are those that we consider promising. Some have relatively active applications today (though not as active as the more established use cases listed above), and others are still relatively nascent.

Customer Service

Chat bots and conversational interfaces are a rapidly expanding area of venture investment and customer service budget (our 2016 AI executive consensus ranked them as the most promising short-term AI consumer application). Companies like Kasisto are already building finance-specific chatbots to help customers ask questions via chat such as “How much did I spend on groceries last month?” and “What was the balance of my personal savings account 60 days ago?”

These assistants have had to be built with robust natural language processing engines as well as reams of finance-specific customer interactions. Banks and financial institutions that allow for such swift querying and interaction might pick up customers from stodgy banks that require people to log onto a traditional online banking portal and do the digging themselves.

This kind of chat (or in the future – voice) experience is not the norm today in banking or finance, but may be a viable option for millions in the coming five years. This application goes beyond machine learning in finance, and is likely to manifest itself as specialized chat bots in a variety of fields and industries.

Security 2.0

Usernames, passwords, and security questions may no longer be the norm for user security in five years. User security in banking and finance is a particularly high stakes game (you’d probably rather your Facebook login to the world than release your bank account information to a small group of strangers, and for good reason). In addition to anomaly-detection applications like those currently being developed and used in fraud, future security measures might require facial recognition, voice recognition, or other biometric data.

See:  A Tech CFO on Three Disruptive Technologies Transforming Finance

Sentiment/News Analysis

Hedge funds hold their cards tight to their chest, and we can expect to hear very little by way of how sentiment analysis is being used specifically. However, it is supposed that much of the future applications of machine learning will be in understanding social media, news trends, and other data sources, not just stock prices and trades.

Continue to the full article --> here

 


NCFA Jan 2018 resize - Machine Learning in Finance – Present and Future Applications The National Crowdfunding & Fintech Association (NCFA Canada) is a financial innovation ecosystem that provides education, market intelligence, industry stewardship, networking and funding opportunities and services to thousands of community members and works closely with industry, government, partners and affiliates to create a vibrant and innovative fintech and funding industry in Canada. Decentralized and distributed, NCFA is engaged with global stakeholders and helps incubate projects and investment in fintech, alternative finance, crowdfunding, peer-to-peer finance, payments, digital assets and tokens, blockchain, cryptocurrency, regtech, and insurtech sectors. Join Canada's Fintech & Funding Community today FREE! Or become a contributing member and get perks. For more information, please visit: www.ncfacanada.org

Latest news - Machine Learning in Finance – Present and Future ApplicationsFF Logo 400 v3 - Machine Learning in Finance – Present and Future Applicationscommunity social impact - Machine Learning in Finance – Present and Future Applications
NCFA Newsletter subscribe600 - Machine Learning in Finance – Present and Future Applications

NCFA Fintech Confidential Issue 2 FINAL COVER - Machine Learning in Finance – Present and Future Applications

ConenSys | Dec 3, 2019 On January 3rd, 2009, in the wake of a global financial crisis that accelerated the growing chasm of inequality throughout world economies, a mysterious figure named Satoshi Nakamoto launched a virtual currency named Bitcoin that functioned atop what s/he called a ‘Proof of Work chain.’ In its ‘genesis block,’ Nakamoto permanently embedded a brief line of text into the data that signaled the inspiration behind the newfangled tech: “The Times 03/Jan/2009 Chancellor on brink of second bailout for banks.” It was a rallying cry for a better way. What proceeded over the next decade has been a stratospheric rollercoaster ride for cryptocurrencies and digital assets, alongside the early phases of a total reworking of economic and human systems atop a philosophy of decentralization and democratization of access to value. See:  How Big Data and Blockchain are enhancing FinTech There have been inconceivable highs and corresponding lows in the ten plus years since Bitcoin’s genesis block, as development of blockchain technology and awareness of its potential marches ever forward. As this decade draws to a close, it’s an opportune moment to view ten years of blockchain development in retrospective. The technology has grown from a digital ...
Read More
10 years of bitcoin blockchain - Machine Learning in Finance – Present and Future Applications
Collaborative Fund | Morgan Housel  | Oct 16, 2019 An irony of studying history is that we often know exactly how a story ends, but have no idea where it began. Find something that’s important to you in 2019 – social, political, economic, whatever – and with a little effort you can trace the roots of its importance back to World War II. There are so few exceptions to this rule it’s astounding. But it’s not just astounding. It’s an example of something easy to overlook: If you don’t spend a little time understanding World War II’s causes and outcomes, you’re going to have a hard time understanding why the last 60 years have played out the way they have. You’ll struggle to understand how the biggest technologies got off the ground, and how the most important innovations are born from panic-induced necessity more than cozy visions. Or why household debt has risen the way it has. Which raises the question: What else is like World War II? What are the other Big Things – the great-grandparents – of important topics today that we need to study if we want to understand what’s happening in the world? The three big ...
Read More
Collaborative fund - Machine Learning in Finance – Present and Future Applications
Of Dollars and Data | Nick Maggiulli | Dec 3, 2019 Psychological Tricks for Worry-Free Spending I want to teach you how to spend money.  You may think that statement sounds ridiculous and say to yourself, “Nick, I don’t need help with spending money.  I’m an expert at that!”  But I’m not talking about how to spend money extravagantly.  I’m talking about how to use your hard-earned cash in a worry-free way. There have been thousands of personal finance articles written on how to spend money.  Some of these articles emphasize frugality and reducing your expenses, while others focus on growing your income so you don’t have to worry about expenses at all.  But, the problem with many of these approaches is that they are based upon one thing—guilt. Between Suzie Orman telling you that buying coffee is equivalent to “peeing away $1 million” and Gary Vaynerchuk asking you whether you are working hard enough, mainstream financial advice is built upon sowing doubt around your decision-making.  Should you buy that car?  How about those fancy clothes?  What about a daily latte?  Guilt.  Guilt.  Guilt. This kind of advice forces you to constantly second guess yourself and creates anxiety around spending ...
Read More
spending and shopping - Machine Learning in Finance – Present and Future Applications
Cointelegraph | Anatol Hooper | Dec 5, 2019 Blockchain is transforming the financial industry right before our eyes, with many market onlookers anticipating a complete replacement of existing payment, trading and banking infrastructures. Blockchain and finance seem like the perfect match, but there are other sectors, for which the technology may play a game-changing role. For one particular industry, the latter adjective isn’t figurative at all, because blockchain can do just that – change the gaming market. This is a unique chance for investors, and it seems like they don’t want to miss it. During the last few years, the gaming industry has been pampered with several innovations at once – virtual reality (VR), augmented reality and artificial intelligence. But it is blockchain that can have the greatest contribution, bringing more transparency and trust to the gaming space. Investors don’t want to be simple observers and are jumping on the blockchain gaming bandwagon. For them, the technology has a disruptive potential that can be converted into profitable deals. Thus, they consider this emerging technology to be a breakthrough in the gaming industry. See:  Podcast Ep27-Mar 1: Blockchain Gaming and Esports with Shidan Gouran Transforming gaming at all levels But how can ...
Read More
blockchain gaming - Machine Learning in Finance – Present and Future Applications
Financial Post | Julius Melnitzer | Dec 5, 2019 Canadian banks have become 'incubators and accelerators' for tech talent, helping to get new innovations to market more quickly For all the buzz about the disruption that’s occurring in Canada’s financial services sector, the country ranks a lowly 23 among 27 countries in its market adoption of fintech. The information appears in an infographic prepared by Fortunly, an online knowledge base and financial product review-website. The charts examine the significant disruption that fintech solutions are causing in the world of finance, including mobile wallets, cash transaction systems, the rise of blockchain currencies and artificial intelligence. See:  A major UK lender just launched a digital bank to compete with Monzo and Revolut | Interview with Bó CEO Which is not to say that Canada is standing still. The country’s market adoption rate of fintech stands at 50 per cent, not insignificant but still way behind China and India, leading the pack at 87 per cent. Rounding out the top 10 are Russia and South Africa, Colombia, Peru, Netherlands, Mexico, and Ireland and the United Kingdom. Canada’s adoption rate, however, is ahead of that in the United States, France and Japan. Globally, adoption ...
Read More
fintech and banks mashup - Machine Learning in Finance – Present and Future Applications
Entrepreneur | Murray Newlands | Dec 3, 2019 You can raise a million, too. Here's how to be successful with equity crowdfunding. There's an art to raising money for a startup. I recently joined Commerce AI as Chief Strategy Officer, and my role has two main functions: fundraising and marketing. My goal in the first 30 days was to raise a million dollars from crowdfunding. This can be a viable goal for your company as well. Here’s how. Equity crowdfunding Under the Jumpstart Our Business Startups (JOBS) act, there are a number of routes to crowdfunding. The starting point is a Form C round, which in essence means you can raise $1.07 million per year -- yes per year -- from non-accredited investors. This means anyone can invest over $250 at a time. This time we worked with accredited investors only but most people will start with a Form C round. Architecting a New World: Investment Crowdfunding and Digital Assets This model is like Kickstarter, but you give backers equity rather than a product. The equity can be a convertible note, a safe note or a fixed price round. If your goal is to raise more than $107,000, an independent CPA ...
Read More
equity crowdfunding funding - Machine Learning in Finance – Present and Future Applications
Novacap | Release | Dec 3, 2019 Novacap is the first private equity firm in Canada to launch a fund dedicated to financial services. MONTREAL, Dec. 3, 2019 /PRNewswire/ - Novacap, one of Canada's leading private equity firms, announced the introduction of a new sector fund and its first closing. Novacap Financial Services I (the "Fund") gathered initial commitments of C$260 million, a strong start toward its target of C$500 million. A second group of institutional investors is expected to close in Q1 2020. Driven by strong demand from new and existing investors, the Fund will be managed by three seasoned executives: Marcel Larochelle, as Managing Partner, as well as Rajiv Bahl and Alain Miquelon as Senior Partners. With a dedicated investment team, they will fully leverage Novacap's infrastructure and apply Novacap's proven investment methodology. Novacap Financial Services I aims to invest in mid-market companies established in North America, with a focus on Canada, with strong growth potential.  Four segments are of particular interest: 1-specialty insurance and distribution, 2-asset and wealth management, 3-alternative lending and 4-financial infrastructure. The Fund will make equity investments in order to support companies with their organic growth initiatives and to drive strategic acquisitions. See:  Portag3 Ventures ...
Read More
Novacap - Machine Learning in Finance – Present and Future Applications
Brookings Institution | Dec 4, 2019 Facial recognition technology has raised many questions about privacy, surveillance, and bias. Algorithms can identify faces but do so in ways that threaten privacy and introduce biases. Already, several cities have called for limits on the use of facial recognition by local law enforcement officials. Now, a bipartisan bill introduced in the Senate proposes new guardrails for the use of facial recognition technology by federal law enforcement agencies. See:  Smart Cities Offer Promises and Concerns Over Privacy On Thursday, December 5, the Center for Technology Innovation at Brookings will feature Senators Chris Coons (D-Del.) and Mike Lee (R-Utah), who introduced the bipartisan Facial Recognition Technology Warrant Act this past November. The discussion will focus on how placing procedural safeguards on facial recognition technology, such as requiring warrants and limiting the duration of surveillance, can alleviate concerns over security and privacy while encouraging innovation. Thursday, Dec 05, 2019 8:45 AM - 9:30 AM EST Brookings Institution Falk Auditorium 1775 Massachusetts Avenue N.W. Washington, DC 20036 More information on registering for the Webcast or attending --> here The National Crowdfunding & Fintech Association (NCFA Canada) is a financial innovation ecosystem that provides education, market intelligence, industry ...
Read More
facial recognition - Machine Learning in Finance – Present and Future Applications
Wealthsimple | Isabelle Kirkwood | Dec 2, 2019 Toronto-based FinTech startup Wealthsimple is separating its direct to consumer and Wealthsimple for Advisors businesses and will transition the advisor-focused offering to a new company, BetaKit has learned. “We’re currently focused on identifying the right partner to support your business on a future platform.” Wealthsimple for Advisors is the company’s automated management platform targeted toward financial planners, investment advisors, portfolio managers, and dealers. The company announced the news to separate the entities in an email obtained by BetaKit and sent to clients on Monday. Wealthsimple plans to announce the move on Tuesday morning. In a statement to BetaKit, the company noted that Wealthsimple for Advisors will transition in the coming months, and is currently looking for partners to support advisors on a new platform. See:  Wealthsimple launching zero-commission trading platform “We are at a pivotal stage in our business where we have a very real, very unique, once-in-a-generation opportunity to transform financial services for Canadians,” said Michael Katchen, co-founder and CEO of Wealthsimple, in the statement to BetaKit. “To take full advantage of that opportunity, we need to be laser-focused on delivering transparent, accessible financial services to consumers, both directly and in ...
Read More
Wealthsimple coin - Machine Learning in Finance – Present and Future Applications
OECD | Mats Isaksson | Nov 27, 2019 27/11/2019 - Asia is rapidly growing into the world’s largest stock market. In 2018, 51% of all equity capital raised through initial public offerings (IPOs) went to Asian companies. Today more than half of the world’s listed companies are from Asia. This development is reshaping global stock market in several ways, according to a new OECD report: Households outside of Asia have increased their investments in Asian companies through pension funds, mutual funds and other intermediaries; it is increasingly common that listed companies are majority owned by the public sector or by other private companies; and smaller growth companies from Asia are using capital markets to raise money more extensively than smaller companies from the rest of the world. See:  Social equity must be central to urban tech innovations OECD Equity Market Review of Asia 2019 says that Asian non-financial companies raised an annual average of USD 67 billion during the last decade. This means that they surpassed the combined amount of equity raised by companies from Europe and the United States.‌ The development in Asia is largely due to a significant increase in the use of public equity markets by companies ...
Read More
OECD equity market review Asia - Machine Learning in Finance – Present and Future Applications