AI And The Future Of Finance: Digital Banking, Algorithmic Trading, And Financial Innovation

Artificial intelligence (AI) has evolved so quickly that we barely notice it. The mysteriously veiled technology often works for us behind the scenes, simplifying tasks and complementing human interactions with others and the world.

AI habitually does tasks for us that we may not even consider. From using advanced algorithms to process large amounts of data that help the technology learn and assist us more efficiently moving forward, AI is always helping and learning.

Whether we notice it or not, technology continues to permeate our day-to-day routines, even when it comes to our money. AI is increasingly present within banking apps, providing services like facial and voice recognition, sending notifications, detecting fraudulent activity, document reading and processing, predictive analytics, more precise risk assessment, and user experience optimization. AI integrated into banking apps can also monitor and examine our spending habits, personalizing recommendations, such as investment advice, credit offerings, and personal financial management options.

Additionally, the rise of algorithmic trading allows for high-speed decisions that capitalize on minute market inefficiencies.

Undoubtedly, AI and big data have profoundly transformed our lives and how we do business. Technology has touched every facet of the consumer world, ushering in a new era of innovation and efficiency. And despite several global setbacks, it’s not expected to slow down soon.

According to Global Market Insights, the BFSI market has a projected growth of 20% CAGR from 2023 to 2032 due to increased investments in AI solutions and services. The publication states, “Global investments in the FinTech sector grew by over 68% in 2021 compared to 2020.” The impact of AI in the BFSI market was valued at $20 billion in 2022. According to Global Newswire, AI in banking accounted for $6.82 billion, fluctuating upward to $9 billion in 2023.

The Russia-Ukraine war caused a temporary disruption in global economic recovery from the COVID-19 pandemic because supply chain disturbances and commodity price surges led to inflation taking hold of many markets worldwide. However, steady growth is expected to continue at a CAGR of 32.5% from 2023 to 2027, with AI in the banking market estimated to reach a whopping $27.76 billion throughout those four years.

AI is superior at tackling projects that are “too much for human minds,” according to a publication by Maryville University in St. Louis, MO. Sorting and analyzing big data is one of those projects—one that can significantly influence decision-making within any organization or institution.

In short, data is needed to help AI mature, and AI is needed to sift through and process copious amounts of data. Therefore, AI and big data go hand-in-hand—effectively gathering insights, predicting upcoming trends, gaining a competitive advantage, and achieving desired outcomes.

According to the same university publication, Forbes cited research that indicates pairing AI and big data can yield significant results, automating “nearly 80% of all physical work, 70% of data processing work, and 64% of data collection tasks.”

Consequently, these technologies are upping the ante of the financial services industry.

Here are some ways AI and big data are impacting the financial world:

  • Risk Management and Fraud Detection: AI can identify patterns of fraudulent activities by analyzing vast datasets. This method has proven more effective than traditional methods in thwarting fraud attempts.
  • Algorithmic Trading: Algorithms process a lot of data fast to make rapid trading decisions. This AI capability has increased trading volume and efficiency in the stock market.
  • Personalized Banking: By using big data analytics, banks can understand consumers’ spending habits and financial behaviors, allowing them to offer tailored products, advice, and discounts.
  • Credit Scoring: AI models can evaluate creditworthiness by analyzing non-traditional data sources like social media activity. This insight could provide more accurate credit scores or offer opportunities to previously underserved populations.
  • Chatbots and Virtual Assistants: These tools instantly respond to customer inquiries, reducing operational costs and improving customer service.
  • Wealth Management and Robo-Advisors: AI-driven platforms give investment advice based on algorithms, making wealth management services available to a broader audience at a lower cost.
  • Operational Efficiency: Automating back-office tasks like data entry, compliance checks, and other repetitive tasks can lead to cost savings and increased efficiency.
  • Predictive Analytics: Financial institutions use AI to forecast market trends, customer behaviors, and potential economic shifts, aiding decision-making aligning with future goals.
  • Regulatory Compliance and Monitoring: AI can automate compliant transactions and operations with regional and international regulations. It can also assist in regulatory reporting.
  • Enhanced Customer Experience: AI-driven analytics make it easier for financial institutions to gain insight into customer preferences and behaviors, leading to better product recommendations and user experiences.
  • Blockchain and Smart Contracts: Combining AI and blockchain can automate and verify contract performance and transactions for better transparency and efficiency.

As brilliant as it is, AI is not a solo technology, meaning humans must actively govern, monitor, and update it so it continues to be helpful. While its many benefits typically offset any risks or challenges, it is not failproof. Therefore, it is necessary to recognize and mitigate AI-related risks so they don’t present an ongoing headache.

Challenges you should be aware of when jumping on the AI ship include:

Overreliance on Algorithms

Sometimes, too much of a good thing is a bad thing. For instance, overdependence on automated decision-making can lead to significant errors if the AI encounters unfamiliar data or conditions. Human intelligence is still highly encouraged—that’s what AI hopes to imitate.

Additionally, putting too much faith in technology to run operations could trigger systemic risks if many institutions use similar algorithms. The idea is to stay ahead of the competition, not trail along with the whole lot.

Model Risk

It’s all about the input. If the AI model is trained on biased or incomplete data, it can produce inaccurate predictions. This is no small thing in the financial world. Such inaccuracies could yield substantial monetary losses, which could be difficult to overcome.

Transparency and Accountability

Technology can only communicate with us to the extent we have programmed it. Many AI models, especially deep learning models, are considered “black boxes,” meaning it’s hard to understand how they make decisions.

Often, in finance, explanations for decisions are mandatory, especially when it comes to credit or investments. Therefore, this lack of transparency initiated using AI models can sometimes prove problematic.

Data Privacy and Security

Data steers planning, implementation, and results. In other words, data is a big deal. AI relies heavily on quality data for peak outcomes. Therefore, keeping data safe is a priority. Mishandling or breaches of data can harm customers and result in regulatory penalties.

Job Displacement

There’s a lot of fear surrounding automation, with many harboring the mistaken belief that robots will eventually replace humans in the workforce entirely. One article referenced a 2013 paper by two Oxford academics claiming job loss in the U.S. due to advancing technology is a real concern. The scholars surmised that nearly 50% of jobs will be replaced by automation by 2033. Over half of the jobs (54%) in supposed danger are in finance.

Still, others believe that robots entering the workplace create more jobs than those they eliminate. Harvard Business Review estimates that about 97 million more roles will become available due to the rise of robots, terming these new positions the “jobs of the future.”

Even with the creation of new jobs, though, automating tasks in the financial industry could put certain existing jobs at risk. Jobs at higher risk of becoming obsolete include many customer support roles, back-office operations, and even some analyst positions.

Ethical Concerns

It’s possible AI might inadvertently discriminate against certain groups of people, especially if it’s trained on biased data. This could lead to unfair loan denials or unfavorable rates for some individuals, with marginalized communities most likely to suffer the downsides of AI in finance.

Of note, discriminatory practices of AI within financial institutions, whether intentional or not, can have severe consequences. A 2023 article published by a law firm regarding AI bias states: “If federal banking regulators identify patterns or practices of discrimination in a financial institutions’ AI, it could result in referrals from those regulators to the Department of Justice for enforcement.”

Flash Crashes and Systemic Risks

Speed isn’t always ideal. High-frequency, AI-powered trading can lead to flash crashes in the stock markets, causing rapid and significant losses. According to Wall Street Mojo, “A flash crash is a financial event in which a rapid withdrawal of stock orders or sales leads to a sudden and drastic fall in prices.” This drop is typically followed by a fast recovery, usually within the same day. However, it can still have steep consequences.

In 2010, a flash crash resulted in only partial rebounds for leading U.S. stock indices like the Dow Jones Industrial Average, S&P 500, and Nasdaq Composite Index. The trading was described as “extremely turbulent,” with many losses not fully recovered. By the end of the day, even with more than half of the losses accounted for, it’s estimated that this singular incident diminished the market value by around $1 trillion.

Regulatory Challenges

Once again, speed becomes a problem when the rapid evolution of AI in finance often outpaces regulatory frameworks. This disparate situation can lead to gaps in oversight that can yield potential issues that may or may not be easily resolved.

Dependency on Technology

Technology should complement human capabilities and contributions, not run the whole show. Increased reliance on AI systems makes financial institutions more vulnerable to technical glitches, system outages, and cyberattacks. This is another reason why people are still relevant—to keep an eye on what’s happening.

Human intervention is still needed to tend to business appropriately. It should be a two-way street and not either-or.

Feedback Loops

When something’s trending, everyone wants a part in it. However, with AI, conformity can be a problem. Many financial institutions might react similarly to certain market conditions using similar AI models and data sources. In turn, this can amplify market trends and potentially destabilize financial systems.

Despite the risks, the benefits of AI are clear. Today’s world requires digital change to stay competitive. Consumers expect it, so it’s essential to deliver. However, regarding the finance industry, Harvard Business Review (HBR) asserts that this change may be more “incremental… rather than transformational.”

The finance industry is a guinea pig of sorts when it comes to realizing AI’s overall influence versus its inherent limits. According to the HBR article, financial institutions often invest in technology and data resources ahead of other industries, providing use cases that may prove helpful in understanding how AI can best assist its human predecessors. And while AI has wielded its transformative powers in some areas of finance, humans are still most effective in others.

Asset management is one area where AI can shine, replacing active fund managers with passive ones. Over just eight years, that ratio has risen from 0.6 to 1.2, per the 2023 publication. The reason is the difference in the speed at which large amounts of data can be sifted through to retain a competitive advantage. Passive fund managers have shown that saving money (valued at one-tenth the cost) is possible while making fast decisions equating to strategies like active fund managers.

In addition, due to the ebb and flow of data, short-term strategies can beat out long-term investment decisions when that data can be extracted and analyzed daily or by the hour. In other words, people no longer need to make one-off decisions with their investments and can change as quickly as the market demands, leading to a more controlled, real-time approach to a person’s finances.

However, the need to take it slow hinges on discussing hard versus soft data, according to HBR. Both are important when making fully informed decisions in finance. AI is excellent at managing hard data that moves quickly, like real-time credit transactions or stock price fluctuations. However, soft data that requires more human intellect or reflective judgment shouldn’t be discounted regarding money handling.

The same applies to accounting, where AI is starting to make strides. However, it still has its limits, specifically with account reconciliation for businesses where revenues are rampant online, according to a 2023 publication by Thomson Reuters. Most of AI’s success in this and other areas where the technology is still learning depends on the information we supply. If that data is lacking in some way or highly unstructured, it’s more difficult for AI to make sense of it and formulate it into a rule. Likewise, per the publication, “a universal ChatGPT-style AI solution… isn’t on the immediate horizon” for tax and accounting sectors or many finance sectors, generally.

Therefore, balance is critical in financial services powered by AI—knowing where and when to invest in AI technologies that boost a financial institution’s potential rather than hinder it. The catch is to provide optimal AI-powered services for clients while not eliminating the human connection that’s imperative to build trust with customers dealing with delicate decisions about wealth management. Digital disruption has become the norm, but its inevitability doesn’t mean skipping out on personalization and functionality, which are key to increased and continued engagement.

The necessary equilibrium likely varies from one financial institution to the next, but discovering what it is can be a game changer for any company’s bottom line.

Forbes reported that 65% of senior financial management acknowledged that AI-powered financial services could inspire positive change. However, many companies are still hesitant to make the shift. A lot of indecision, uncertainty, reluctance, or apathy could be due to the risk of the unknown. This is where Oxford comes in.

We have the knowledge and expertise to get you up and going. AI implementation doesn’t have to be complicated, nor does it need to be filled with fear of the unfamiliar. We can walk you through the process, remaining with you every step of the way.

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Let’s start revamping how you handle your customers’ finances using AI-powered financial services. We will help you stay one step ahead of the competition with both feet in the future, always looking forward. We believe in what’s coming—let us help you get there.