Machine Learning in Finance

The value of machine learning in finance is becoming more apparent by the day. As banks and other financial institutions strive to beef up security, streamline processes, and improve financial analysis, ML is becoming the technology of choice.

Machine Learning – Not Just Another Buzzword

Unlike so many hyped technologies and overrated buzzwords, machine learning is not going away — probably ever. The ability of computer programs to learn on their own and improve over time creates new opportunities for industries across the board.

In this article, we examine 10 ways ML technology is transforming the financial sector. By all accounts, this list will grow exponentially over the next few years.

Fraud Prevention

Financial service providers have no greater responsibility than protecting their clients against fraudulent activity. But for every $1 lost to fraud, financial institutions pay $2.92 in recovery and associated cost.

To win the war against financial fraud, financial companies must abandon outdated approaches. Identifying and preventing fraudulent transactions requires sophisticated solutions that can analyze high-volume data. Machine learning offers such a solution.

Machine learning algorithms can block fraudulent transactions with a degree of accuracy not even possible with stand-alone AI. It is done by spotting patterns and using predictive analytics.

By far this is one of the important application of machine learning in finance.

Risk Management

2017 and 2018 saw financial institutions adopting ML solutions for financial risk management.

Traditional software applications predict creditworthiness based on static information from loan applications and financial reports. Machine learning technology can go further and identify current market trends and even relevant news items that can affect a client’s ability to pay.

Of course, risk management also extends to preventing financial crime and financial crisis prediction. Machine learning in finance provides solutions to these and many other risk concerns.

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 are algorithms built to calibrate a financial portfolio to the goals and risk tolerance of the user.

Users enter their goals, age, income, and current financial assets. The advisor 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 are a hit 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.

Investment Predictions

In recent years, hedge funds have increasingly moved away from traditional analysis methods. Instead, they have adopted machine learning algorithms for predicting fund trends.

AI and ML are now able to bring the best of passive and active investing worlds into one portfolio. The ability to trade risk and reward at the turn of a dial will revolutionize asset management. It will enable institutional investors and fund managers alike to take complex positions in the market.

Both consistent and stable returns or high risk for high reward conditions can be programmed into a dedicated AI engine. It can then be adjusted by the investor over time, as their risk tolerance often shifts over the long-term.

The potential of machine learning technology to disrupt the investment banking industry is being taken seriously by major institutions. JPMorgan, Bank of America, and Morgan Stanley are developing automated investment advisors, powered by machine learning technology.

Network Security

Data security is at the top of the list whenever financial institutions are asked about their concerns. And based on the number of breaches in recent years, they have reason to worry.

The challenge to identify modern sophisticated cyber attacks cannot be relegated to yesterday’s security software. To meet the security threats financial institutions now face requires advanced technology.

Machine learning security solutions are uniquely capable of securing the world’s financial data. The power of intelligent pattern analysis, combined with big data capabilities, gives ML security technology an edge over traditional, non-AI tools.

Loan Underwriting

A growing number of insurance companies have turned to machine learning to help identify risks and to help set premiums. Since machine learning makes predictions based on historical patterns and current trends, it is the perfect vehicle for insurance companies to improve profitability.

The same advantages apply to the banking sector. Financial institutions that offer insurance products to their clients yield the same benefits from ML as insurance companies.

Whether an institution offers loan protection, health, mortgage, or life insurance, machine learning can help manage risks.

Algorithmic Trading

Sometimes called Automated Trading Systems, AT involves the use of complex AI systems to make extremely fast trading decisions. In its simplest form, an “algo” trade can automatically buy (or sell) a quantity of stock when the price-per reaches a specific level.

Machine learning technology offers a new and diverse suite of tools to make algorithmic trading more than automatic. ML makes algo trading intelligent.

ML algorithms analyze historical market behavior, determine an optimal market strategy, to make trade predictions, and more. Without ML, even AI cannot offer that.

There some noted limitations to the exclusive use of machine learning in trading stocks and commodities.

Process Automation

As financial institutions transition from spreadsheets to cloud-based data storage, a tremendous opportunity emerges.

Even though blockchains can automate many processes through smart contracts, they have limitations. Fintech companies will add a machine learning layer to their data processes to maximize their operational efficiency.

ML can do more than automate back-office and client-facing processes. It can interpret documents, analyze data, and propose or execute intelligent responses. The predictive power of ML goes further and identifies issues that will need human attention before they occur.

Document Interpretation

Machine learning is proving that even lawyers are not beyond the disruptive reach of AI.

J.P. Morgan Chase bank has invested $9.6 billion in machine learning, which has already netted a huge payoff. The first fruits of their investment is Contract Intelligence, or COiN, which uses machine learning to interpret documents.

COiN reviewed 12,000 commercial credit agreements and provided analysis in a matter of seconds. What makes that remarkable is the same task takes 360,000 attorney hours to complete.

That makes J.P. Morgan Chase is so happy with machine learning they can hardly count.

Trade Settlements

Trade settlement is the process of exchanging payment and the purchased security following a stock trade.

However, a number of issues can cause a trade to not complete.

Modern trading platforms and regulatory requirements have reduced trade failures to a small percentage. Even so, in high volume trading, failed trades can still affect the efficiency of the system.

The use of machine learning solutions can not only identify the cause for a failed trade but can provide a solution — usually within a fraction of a second. Even better, by identifying exceptions to normal trading patterns, ML can predict which trades are likely to fail.

Money-Laundering Prevention

An estimated 2%-5% of the global GDP is laundered annually. Unfortunately, banks lose the battle many times.

Machine learning offers a long-needed solution to the age-old problem. ML is capable of identifying patterns that are unique to money laundering. ML software results in greater detection rates, fewer false positives, and easier regulatory compliance.

Amsterdam’s Commerzbank plans to use ML to automate 80% of its compliance checklist processes by 2020. The process will begin by focusing AI technology on detecting money laundering.

Conclusion

Machine learning, Artificial Intelligence (AI) and big data are the current trends that are changing the way we the finance industry operates. There are industries and sectors that have already successfully embraced AL and ML and reaping its benefits. And finance will soon be a part of this phenomenon.