6 Ways AI Will Change The Finance Industry In 2023

Artificial intelligence (AI) is an idea - or more accurately a set of techniques or systems - whose time has come and it’s here to stay. 

Yes, those are words you have probably read or heard before. But in 2022, AI wass already delivering value in a host of ways for organizations in almost every vertical you can imagine. From recommending movies to finding patterns in data that help diagnose Covid19, AI turns up where you might expect it, and in plenty of places where you might not. So how will things be different in 2023?

AI systems can be trained to perform a wide range of tasks, from simple tasks like recognizing speech or images to more complex tasks like driving a car or translating languages. AI is being used in a variety of fields, including finance, healthcare, education, and transportation, and is expected to have a significant impact on many aspects of our lives in 2023 and in the coming years.

Of course, the finance industry is one of the major sectors where AI is already used in a variety of ways, and is already adding real value to any number of processes and products in the world of finance. And in 2023, that trend is only going to accelerate, to the point where AI use is very much mainstream. In this piece, we look at 6 specific areas (chosen almost at random, as the number of possible applications is almost infinite) in which AI is set to make a real difference in the year to come.

Trend 1: AI-powered customer service

Attempting to contact a bank is rarely the highlight of anybody’s day. But artificial intelligence can make it a whole lot more pleasant than it would otherwise be. AI is becoming increasingly sophisticated when it comes to interpreting the speech and text of human beings, and as a result AI-powered chatbots are already the first point of contact in many instances.

In 2023, they will most likely be the last. For a huge number of common inquiries, transactions or complaints, chatbots and adjacent technologies are now smart enough to handle the entire process. We’ve come a long way since the days of Windows 97 when we first interacted with Microsoft’s ‘Clippy’. GPT3 (Generative Pre-trained Transformer 3) has already made huge waves in 2022 when it comes to the accuracy and clarity that chatbots answer your questions, and rumors have it that its successor, GPT4, expected to be launched in 2023, will be an even more powerful version of the machine generated human-like text. Already, in many cases AI-powered bots are almost indistinguishable from human support staff: and they don’t forget things or take holidays either.

As a result, banks and other organizations in financial services can now offer high levels of instant customer service, 24 hours a day, 7 days a week, leaving their conventional human experts able to focus on the areas in which they add the sort of value even the smartest bot cannot.

Trend 2: Understanding risk at scale for better investment decisions

Any institution that makes and handles investments is dependent on data: it is what powers smart decision-making and ultimately the success of the business. For the same reason, these organizations employ teams of analysts to search, categorize, evaluate and share information (usually sourced from third-party media) relating to the businesses they invest in. 

Unfortunately, this process does not scale particularly well. As a result, coverage (and hence understanding) is limited: the organization has to rely on incomplete, inaccurate, or out-of-date information as analysts struggle to make sense of the hundreds and thousands of articles published every day around the world.

AI can help. More specifically, Natural Language Processing (NLP) enables this vast media output to be accurately categorized, be accurately and consistently tagged according to subject matter, and shared across the organization almost immediately on publication. As a result, analysts are able to focus on relevant (and only relevant) coverage, and knowledge, insight, decisions and - ultimately - profits are all increased.

Whilst NLP is already used this way in a number of financial organizations, 2023 will be the year we see media coverage handled and distributed via AI by default rather than exception.

Trend 3: Improved fraud detection via AI

Fraud has been an issue in financial services since, well, since financial services first existed. It is expensive, distressing for customers, and sometimes it feels like it is never going away. But artificial intelligence is a powerful tool in identifying and preventing fraud. Already providing great value, in 2023 expect to see AI fraud detection and prevention move into the mainstream.

The billions of financial transactions that take place every day are an almost irresistible opportunity for organized (and less organized) crime. But they also provide the raw material that allow AI and machine learning approaches to rapidly ‘learn’ what fraudulent activity looks like and use that knowledge to block suspicious transactions. In most cases human verification (which in turn teaches the AI) has been required as a secondary step, but we are reaching the point at which the decisions AI makes can be trusted without requiring any manual intervention.

At this point - and in 2022 we are very much there - AI begins to add huge value to any financial institution looking to combat fraud and reduce the costs associated with it.

Trend 4: Automated adverse media alerts

Financial organizations are unusual in being responsible (to some extent at least) for the behavior of their customers. More specifically, banks are legally obliged to check, and continue to check, clients for involvement in money laundering, financial crime, funding of terrorism and so on. Failure to identify and act on these risks can lead to significant fines and reputational damage.

In response to this requirement, the concept of the ‘adverse media check’ has arisen. In the simplest terms, this means checking media and other sources (court records and judgements, for example) for anything suspicious relating to a customer that may require further investigation. But this is a complex, costly, manual process, requiring ongoing monitoring of vast amounts of data. A huge amount is missed, and the automation currently available - simple media scanning - delivers so many false positives that it barely reduces the required effort.

AI can help here, and will help in 2023. By analyzing and categorizing media and other documents using natural language processing, it is able to surface relevant information to relevant individuals in a format they can use, and do so quickly. It is also able to learn as it goes (via human feedback) so that the process becomes more efficient and the number of false positives decreases over time. As a result, it’s easier than ever to monitor customers (both individuals and organizations) and act before it is too late when something comes to light.

Trend 5: Automated investments

We spoke above about improving and automating the flow of information around the organization in order to support improved investment decisions. But in some circumstances, AI can go further, even to the extent of making trading decisions without any human input whatsoever.

In truth, there is a continuum of trading approaches, from entirely human to entirely automated, and AI can play a part everywhere along this spectrum. That should be no surprise: market data amounts to billions of data points, which is fertile ground for any form of machine learning or artificial intelligence. In some cases AI can provide recommendations based on both market and external data (earnings reports, media, etc), in others it can learn to recognize stocks with potential and invest automatically. 

Trend 6: New financial products and services

2023 might be the year when we see a whole new level of personalization when it comes to financial products, financial advice or automated investment portfolios. 

Let’s just think about financial forecasting for a moment. Some companies use AI to analyze market trends and make predictions about the direction of financial markets. This can be useful for investors looking to make informed decisions about their portfolios. Similarly, trading algorithms can analyze market data in real time and execute trades based on predefined rules.

Already there are some financial institutions using AI to evaluate the creditworthiness of loan applicants. This can be done by analyzing data such as credit history, income, and debt levels.

As always, and as in all the examples above, AI truly adds value where large datasets (that humans will struggle to consume, never mind understand) can be used to find patterns and signals that would otherwise go unseen. And 2023 is when that process really becomes reality.