• Sudharson Gunasekaran, Senior Software Engineer at AllianceBernstein


• 14.03.2023 07:15 am
undisclosed

The programming of artificial intelligence (AI) and machine learning (ML) in financial technology (Fintech) has created a revolution that continues to upend traditional systems. As a result, it’s important to be knowledgeable about relational databases (linking key data points), AI databases that have enhanced text analytics and natural language processing support, and other intricacies of emerging technologies. In addition, to help expand the client base, newcomers to the technology need to learn how to harness AI to make lending or trading an easier and more understandable process.

How AI is impacting the finance business sector

Artificial intelligence is an ideal tool for data mining when it comes to determining an applicant’s creditworthiness for a loan or credit card. The information from a person’s credit history is posted digitally, helping financial institutions make faster decisions. Using AI for this task has also led to the rise of fintech firms that consider other non-conventional data including school of study or field of study, employment history, and spending habits. Using this information, loans can be almost instantaneously approved, all without the need for the applicant to visit a bank. Small and medium-size financial institutions can level the playing field with larger competitors by employing AI and ML to harvest the data they need (an alternative to FICO scores) without hiring additional staff to perform those analytical tasks or investing in on-premise server platforms and banks of computer stations. Using cloud-based services (AWS, Microsoft Azure, Google, etc.) to assist with financial tasks will also facilitate and expedite decision-making. 

AI has helped transform the banking and financial services industry in several ways: reducing operational costs by automating routine processes, improving data processing accuracy, providing better customer support, and sharpening the approach to risk management by flagging problem issues (i.e., possible fraud, money laundering, regulatory red flags). AI can also help direct financial institutions when they need to shift investments from one stock or product to another, based on the monitoring of current market conditions. There are considerable cost savings forecasted in the banking/financial services world due to AI. By one industry estimate, the savings are estimated to be $447 billion this year and a trillion dollars by 2030.  

AI as a tool for making financial decisions

One financial AI tool is the robo-advisor. When a consumer uses an app to guide investment decisions, they receive digital messages from robo-advisors about market conditions, with suggestions on changes to their portfolios. This technology often appeals to a younger demographic and the more tech-savvy customers. Some investors may still prefer the personal experience, insight, and historical viewpoint of a professional financial advisor, especially since this nuance is not yet captured by AI. Resistance to AI tools is also seen in the issues some have with ChatGPT and its use in making financial decisions; the app’s cut-and-dry reports do not have that innate intuition. Human capital—the economic value of a worker’s experience and skills—still counts in many circumstances.

Fintech’s short-term future is likely to include tailor-made proprietary AI platforms. Combined with ML, that would allow for affordable access by small and medium-sized financial institutions, including robo-advisors for investment and lending management decisions, algorithmic trading, and smart routing.

Considerations before taking the deep dive into AI

There is typically a downside to any major shift in the way a company does business, which is an important reason to pause before making a hasty AI decision. It can be costly upfront to produce the software programs and algorithms needed to harness AI efficiently, and with continual market condition changes, revisions to that software may be a regular occurrence. Just like with humans, financial robo-advisors can make bad calls, because AI lacks the ability to take individual circumstances into account. AI can also lead to fears of taking power away from veteran human financial advisors or placing it in the hands of the few who control AI functions. 

Another possible downside is the notion that using AI can lead to the elimination of certain jobs in the industry, damaging employee morale. There also are fears that some will lose their creative skills if they rely on AI and ML to make financial decisions. This mindset is not new. The dawn of the industrial revolution in the 1800s resulted in less reliance on manual labor, leading to the loss of employment. Other factors include choosing the appropriate cloud provider to take full use of AI, making sure the program is able to scale up quickly based on the financial institution’s site traffic flow, and that usage costs are in line with the institution’s forecast.

The future of AI in the trading sector

A 2019 study of more than 107 industry professionals revealed that almost 80 percent felt technology had made them more efficient and 70 percent said it provided more opportunities for new entrants to the field. More than half of the 107 online respondents said “incumbents” in the trading business also felt they could expand or improve business practices by embracing “disruptive technologies” like AI to assist clients. A concurrent Greenwich Associates study looked at the “people side” of the AI question more closely. In this study, 83 percent said the technology presented more opportunities, but even 78 percent of baby boomers welcomed the technology for the same reason. Only 24 percent or less of the respondents felt it had replaced functions they were once responsible for. That percentage may have changed over the last few years – or may do so in the future. 

Preparing for AI in the finance world

As with any major technology upgrade, there will be best practices associated with utilizing AI in the finance world as well as pitfalls to avoid for a smoother implementation. Because the revolution is already here, companies can gain a competitive edge by understanding how they can best take advantage of AI for both their needs and those of their clients. It’s important to ensure checks and balances are in place to maintain compliance. From there, organizations can harness AI to expand services to the demographic groups most comfortable with digital banking.