Machine Learning in Finance – Present and Future Applications Posted on : Oct 25 - 2016

Machine learning has had fruitful applications in finance well before the advent of mobile banking apps, proficient chat bots, or search engines. Given 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 loans, 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.

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.

• In this particular article, we’ll explore (in the following order):
• Current applications of artificial intelligence in finance
• Potential future applications of artificial intelligence in finance
• Noteworthy companies operating at the intersection of AI and finance

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. 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 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. 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 TechEmergence 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, etc…) and financial lending or insurance results (did this person default, pay back the loan on time, get in a car accident, etc…?). The underlying trends that can be assessed with algorithms, and continuously analyzed to detect trends that might influence lending and insuring 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. View More