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 Summer Kickoff Event Jul 11 banner resize - Machine Learning in Finance – Present and Future Applications

Yahoo Finance | VeChain Release | June 25, 2019 BEIJING, June 25, 2019 /PRNewswire/ -- On June 25, in combination with Walmart China, China Chain-Store & Franchise Association (CCFA), PwC, Inner Mongolia Kerchin Co., Ltd., and VeChain, the Walmart China Blockchain Traceability Platform, built on the VeChainThor Blockchain, was announced at the 2019 China Products Safety Publicity Week Traceability System Construction Seminar. This seminar was jointly organized by Walmart China and the CCFA in Beijing. The announcement of the Walmart China Blockchain Traceability Platform came with the introduction to the first batch of 23 product lines that have been tested and launched on the Platform. The Platform is expected to scale by another 100 product lines by the end of the year covering more than 10 product categories including fresh meat product, rice, mushrooms, cooking oil, etc. It is expected that the Walmart China's  traceability system will see traceable fresh meat account for 50% of the total sales of packaged fresh meat, traceable vegetables will account for 40% of the total sales of packaged vegetables, traceable seafood will account for 12.5% of the total sales of seafood by the end of 2020. See:  Q&A: Walmart’s Frank Yiannas on the use ...
Read More
walmart and vechain food safety - Machine Learning in Finance – Present and Future Applications
Crowdfund Insider | JD Alois | June 20, 2019 The Securities and Exchange Commission (SEC) has published a statutory report on Regulation Crowdfunding commonly referenced as Reg CF. The mandated report must be forwarded to Congress three years after Reg CF rules became effective (May 2016). Reg CF is the smallest of three federal “crowdfunding” exemptions allowing issuers to raise just $1.07 million from both accredited and non-accredited investors. According to the report authors: “the number of crowdfunding offerings, as well as the total amount of funding during the considered period, was relatively modest.” The report tallies activity under Reg CF from May 2016 to December 31, 2018. At the end of the period, there were 45 active Portals and 9 Broker-Dealers which had participated in at least one Reg CF offering. See: $5 million Equity crowdfunding extended to private companies Early-stage Investing – The Public gets a Seat at the Table Three platforms accounted for two-thirds of all initiated offerings and proceeds raised. SEC: the number of #RegCF #crowdfunding offerings, as well as the total amount of funding during the considered period, was relatively modest Click to Tweet According to the SEC: Between May 16, 2016, and December 31, ...
Read More
RegCF SEC report - Machine Learning in Finance – Present and Future Applications
Chambers Pivot Industries | Greg Chambers | June 20, 2019 "All I need is an investor, and I’m ready to go," she says. I'm sitting in front of a passionate entrepreneur who knows I've successfully raised millions of dollars for various businesses. After hearing her story, what I'm about to say won't be what she wants to hear, but it's true. Funding isn't her problem. There's more money out looking for a home than there are good ideas to fund. The problem, I tell her, is she hasn't decided if she wants to build a company or master the growing seed and startup capital environment. Lessons from the past I was in her seat in the late 1990s shopping my big idea from investor to investor. Eventually unsuccessful, I was forced to abandon my startup and find a job. I took two big lessons from that experience. One is that if I wanted to get a company off the ground, I needed to get much better at selling a vision to investors. Second, based on the questions the investors were asking, I needed far more evidence from customers that my idea was the right one before they’d invest. Years later, ...
Read More
hunter to prey - Machine Learning in Finance – Present and Future Applications
Luge Capital | Karim Gillani | June 2019 Intro:  NCFA Fintech Confidential spoke with some of Canada’s experienced fintech investors, on their background, how Canada has evolved, what we should be doing, advice to fintech founders and what keeps them awake at night.  This is part 1 of a 4 part series.   What is your background, and how did you come to co-found Luge Capital? Karim:  My background is in fintech, mobile tech, engineering, finance and strategy. Prior to Luge, I was at PayPal, leading M&A activities in Canada. I joined PayPal through its $890M acquisition of Xoom, a renowned cross-border remittance company, where I started the Corporate Development practice. I have an Engineering degree from the University of Waterloo, a Master of Finance degree from the University of London and a Master of Laws from the University of Toronto. Luge Capital was the byproduct of highly motivated LPs, and a recognition that fintech venture capital needed a kickstart at the early stages. David Nault and I co-founded Luge in early 2018 with a new model to seek out entrepreneurs in the US and Canada that not only had a drive to take over the world, but also built their ...
Read More
luge capital - Machine Learning in Finance – Present and Future Applications
CDL Team | June 18, 2019 The Libra Association announces a new initiative with the goal of increasing access to financial services and fostering financial inclusion around the world TORONTO, CANADA – Today, Creative Destruction Lab (CDL) – a not-for-profit seed-stage startup program – announces that it will be a Founding Partner of the Libra Association. CDL is keen to contribute to the success of the Libra initiative as the sole Canadian organization and academic institution in the Libra Association at present. The Libra Association will create Libra, a simple global currency and financial infrastructure that can empower billions of people. Libra will be built on a secure, scalable, and reliable blockchain; and it will be backed by a reserve of assets designed to give it intrinsic value. The Libra Association will govern the infrastructure and manage and evolve this new ecosystem. Libra will enable developers and businesses to build inclusive new financial service products for people around the world. See:  Facebook’s Libra Cryptocurrency: Everything We Know At this time, CDL is the sole academic Founding Partner of the Libra Association. The initial group of organizations that will work together on finalizing the association’s charter include: Payments: Mastercard, PayPal, PayU ...
Read More
CDL libra - Machine Learning in Finance – Present and Future Applications
PC Mag | Rob Marvin | June 18, 2019 Facebook's Libra Cryptocurrency: Everything We Know Facebook's big blockchain play, consisting of the Libra coin, the nonprofit Libra Foundation, and Facebook's Calibra wallet, will create a crypto-based payments ecosystem across Facebook, Messenger, WhatsApp, and beyond. Facebook's long-rumored cryptocurrency finally got its big debut, and it's called Libra after all. Facebook today released a lengthy white paper, along with a post from Mark Zuckerberg and another from VP of blockchain David Marcus, announcing the ambitious crypto initiative and all that comes with it. The open-source Libra cryptocurrency and blockchain will be governed by the nonprofit Libra Association, while a new Facebook-owned subsidiary called Calibra will release a wallet for Libra tokens and ultimately other banking and finance products—a move that could turn Facebook into a financial services giant in addition to a social and advertising one. See:  Facebook’s Cryptocurrency: Great Idea, Wrong Company While the public launch of Libra won't happen until the first half of 2020, the developer testnet of the Libra blockchain is live today. There will also be a new programming language called Move for developers to build distributed applications atop the Libra blockchain, though Facebook said neither itself ...
Read More
facebook launches libra - Machine Learning in Finance – Present and Future Applications
3iQ and Mavennet | Fred Pye and Kesem Frank | June 18, 2019 Stablecoins are now a necessary step to mass adoption of cryptocurrencies, as proven by the way they’ve been used to hedge the massive volatility of the market over the past couple of years. Their simple premise enables the seamless pairing of crypto-to-fiat pegged cryptocurrency. It might sound overly simplistic, but this straightforward innovation has spurred the growth of a new crypto asset class that measures in billions of dollars in aggregate market cap (e.g. Tether, USD Coin, TrueUSD, Paxos and Gemini Dollar). As much as this asset class is still gaining momentum driven by the current and common use case, the potential of stablecoins goes well beyond the tactical value of a trading tool. Stablecoins are strategically important because they represent a bridge between legacy fiat-based systems and the new digital and decentralized currency underpinnings we collectively call “blockchain.” The dream isn’t necessarily a prediction or extension of the purist’s vision   Bitcoin - blockchain’s earliest network - was born from tumultuous years in the traditional financial system. These were years defined by mistrust; not just towards the people at the helm of the financial system, but ...
Read More
stablecoins  - Machine Learning in Finance – Present and Future Applications
CNBC | Kate Rooney and Hugh Son | June 10, 2019 Mobility giant Uber is looking to accelerate the creation of financial products with a new fintech outpost in New York, according to people with knowledge of the plan. The ride-hailing company is aiming to hire several dozen engineers and product managers this year, and the New York team could eventually exceed 100 workers, said the people, who declined to be identified speaking about Uber’s plans. Uber, fresh from its IPO last month, is looking to tap New York’s talent pool, which is deeper when it comes to fintech and bank workers than its hometown of San Francisco. By building out its financial ecosystem, the company can increase its lead over rivals like Lyft. The efforts are likely to be focused on ways to increase engagement and loyalty to the Uber platform, according to people who attended a recruitment event earlier this year. Payments chief Peter Hazlehurst and top engineer Johnie Lee spoke at the event, held at Uber’s New York offices, the people said. There are many possible payment and lending innovations Uber could come up with: It has 93 million active users globally, most of whom use linked ...
Read More
Dara Khosrowshahi CEO uber tech - Machine Learning in Finance – Present and Future Applications
Schulte Research | Paul Schulte | June 17, 2019 Digital banking finally arrives in HK --all in one go! Be careful what you wish for — you might get it.  Hong Kong People have been kvetching for years about the poor quality of banking services. Now, they will have a deluge of ultra-efficient and essentially free new services. These services will offer strictly online banking services without branches and ALL of them have very deep pockets. The first batch below, which I will enumerate in a moment, have capital to burn of about USD 250 million. This can go a long way in eroding the highly profitable cartel of HSBC and Hang Seng Bank.  Hang Seng Bank has consistently had among the highest ROE globally north of 20-21%. And its revenue per customer has been among the world’s highest as well. HSBC owns more than 40% of HSB, so it has been a cash cow for the bank. Hong Kong is really the center of profitability for HSBC, since its ROE for commonwealth countries is the single digits and it has basically given up on the US financial market.  It’s European business, like all Euro banking franchises, is in the ...
Read More
global network and points of presence maps - Machine Learning in Finance – Present and Future Applications
Forbes | Enrique Dans | June 17, 2019 All the signs are that Facebook is about to launch its cryptocurrency on June 18, a project known internally as Libra, and that soon, apparently, we will all be using. So what are the implications of a company with 2.4 billion users launching its own currency? Strategically, the movement makes sense for Facebook: at a time when many question the its dominance of social networks and when a majority of its own shareholders say they want to see the back of Mark Zuckerberg, the company announces a very ambitious project of universal appeal giving it a central role in the world economy, in the wake of innumerable cryptocurrency projects of dubious legality, irresponsibly speculative and wasteful in terms of energy, aimed among others at people in countries with unstable currencies or limited banking penetration. As Jack Dorsey has said, this maybe the perfect moment to create a universal currency for the Internet era, reflecting the trend toward a universalization of the world. However, what is less clear is whether this currency should be in the hands of Facebook. See:  FaceCoin: Here’s What Facebook Could Build In Blockchain And Cryptocurrency Technically, the project ...
Read More
mark Z. facebook - Machine Learning in Finance – Present and Future Applications