Machine Learning in Finance: 7 Applications and Their Global Footprint

The credit markets are booming. In the United States alone, the amount of consumer credit outstanding has increased by 22% from 2014 to 2019, surpassing $4 trillion⁴. The total bank credit (US commercial banks) has grown by $3 trillion in five years, reaching $13.3 trillion in March, 2019⁵.

In these circumstances, banks and other financial institutions are doing their best to balance their risk appetite with the opportunity. This creates a competitive environment where financial institutions don’t want to miss out. Somewhat loosened regulations also mean that banks are willing to take riskier loans⁶.

Artificial intelligence in finance is one of the latest and most rapidly adopted additions to the toolboxes of these organizations, as they race to capture the best credit opportunities or understand bad ones that are already in their portfolios. In the 2018 WEF report, 73% of financial services and investment companies surveyed were to adopt machine learning by 2022⁷.

Let’s take a closer look at some of the specific use cases and processes this technology advancement is going to cover.

The majority of banks globally employ relatively manual credit review and allocation processes. Document reviews, credit risk calculations, and form submissions are done manually or semi-manually in many banks. That’s why it may take hours to process credit applications, especially if we’re talking about mortgages or other non-trivial cases. For this reason, mortgages are usually underwritten in no less than three days¹¹.

Automating loan processing can save significant amounts of money and tremendously shorten the time it takes to process a credit application. Many financial institutions are already reaping the benefits of artificial intelligence, which can help with automation.

For example, Citigroup successfully uses artificial intelligence, although for corporate loan processing, to analyze applicants’ financial statements¹². Implemented first at their Hong Kong credit analysis department as a trial project, this project has already helped the global banking giant reduce their load processing times from 10-15 days to just 2 days.

Capital Float in India automatically assesses credit risks, creates early warnings for loans that are likely to go bad, and achieves many other benefits with the help of AI systems that process all of this data and build risk predictions¹³. They’re treating the whole process as something that can be 100% automated if they push long and hard enough.

In Norway, Kraft Bank is also trying to expand automation throughout its credit management pipeline with the help of Tieto¹⁴. It is not a surprise, since there are plenty of other use cases for machine learning in finance, and specifically in credit processing:

These applications are just a part of the overall trend across many markets and many types of financial institutions. Competition is one of the biggest driving factors in this process.

We’d like to point out that reinforcement learning is a relatively new practical field of AI in finance, with a long history of theoretical discoveries but a small period of real-world implementations²¹. The essential difference from other machine learning domains is the approach to analysis and results.

Reinforcement learning seeks to reward the system for the right decisions, whereas the more traditional machine learning approaches are trying to infer the right decision based on the provided data²². Reinforcement learning systems change with each new data point, while traditional machine learning requires all of those data points to be available in advance.

The long-term benefits of the reinforcement learning applications in trading aren’t yet completely obvious. However, that doesn’t stop companies and institutions from exploring this potential. Reinforcement learning is being tested as a prime tool for creating trading policies, as it doesn’t have any set rules to follow and can develop its own trading “game plan.” As a result, reinforcement learning can develop its own action policies and respond to real-time market changes in a more sophisticated manner.

Reinforcement learning is a promising development in AI that can completely change how trading is done. However, at this point, it is pioneered only by a handful of companies and a few Kaggle enthusiasts²³. Luckily, there are plenty of applications of “traditional” AI/ML systems in trading.

The wealth management market is another booming financial domain. Here, the market of wealth management platforms alone is projected to grow from roughly $1.5 billion in 2016 to $3+ billion in 2022²⁹.

Ernst & Young place technological innovations among the key driving factors that will shape this expanding financial industry³⁰. Of course, AI has its spot among these technologies. Wealth management clients think that applying machine learning in finance will have a profound effect on the market and its players³¹.

Consider a scenario: an advisor has a meeting with a customer who wants to invest in a certain company. Before that initial meeting, the advisor’s job is to acquire, analyze, and structure as much data about the potential investment as possible. We’re talking about dozens of documents. And, as you already know, the advisor might have dozens or even hundreds of other clients to take care of. The quality of services is at stake, as well as the advisor’s job satisfaction.

Artificial intelligence in finance can be used to assist them with this challenge. The agent can use AI to summarize documents³⁷, extract additional insights, like sentiment³⁸, or look for mentions of specific financial KPIs. This data can help them compile a holistic representation of the investment target and tailor financial advice to that knowledge. All of these efforts could save dozens of hours per year, which would have been otherwise spent droning over data instead of performing more creativity-demanding work.

Machine learning in finance is emerging as one of the most noteworthy innovations. Here are the takeaways summing up its visible impact across the domains:

• Credit markets are embracing AI in pursuit of new risk management capabilities. Here, AI stretches to loan data analysis and credit risks review.
• Trading companies don’t miss out on AI adoption either. Increasingly based on reinforcement learning algorithms, AI trading applications are favored as they can effectively operate with high predictability around the clock.
• In wealth management, AI is indispensable for creating relevant personalized experiences, in many cases through cognitive agents. It is also finding more applications in uplifting advisory services and serving to-the-point product recommendations.
• Despite its big promise, AI in finance still needs to be used cautiously. It is highly data-dependent, and its effect is only as good as the data fed into it.