The State of AI in Financial Services in 2023

Is the financial services industry facing an AI arms race in 2023?

Following the 2022 release of OpenAI’s ChatGPT, AI has skyrocketed in terms of mainstream awareness and consumer curiosity. Although AI solutions for the financial services industry have been a thing for years now, ongoing discussions about ChatGPT in financial markets have brought about new questions regarding the use cases, benefits, and risks of AI in the banking and financial space.

Insider Intelligence reports retail banks alone are expected to spend $4.9 billion (USD) on AI platforms by 2024. Additionally, inquiries regarding AI in banking have increased five-fold in the first quarter of 2023 compared to the same period the year prior.

With advancements in AI technology occurring rapidly and public interest following steadily, financial service providers must consider how to optimize their approach to AI implementation—and quickly.

The Story So Far: Advancements in Financial Services Driven by AI

The financial services industry is no stranger to AI.

The earliest adoption of the technology within the industry dates all the way back to the 1980s. During this time, one of the most prominent AI-based products was the Expert System, an early version of a computer system designed and programmed to emulate human intelligence in its processing capabilities.

According to a 1988 Harvard Business Review article, Expert Systems helped differentiate products by lowering costs and providing organizations with a deeper understanding of various technical and operational challenges. The article goes on to state that Expert Systems showed the most potential as applications for optimizing “mundane tasks.”

Today, AI in financial services has become a necessary digital tool for keeping up with the digital era of finance.

In the recently published NVIDIA report the State of AI in Financial Services: 2023 Trends, 500 global financial services professionals were asked how AI has created value for their organizations. The top answers to this question were as follows:

• Improved customer experience (46% of respondents)
• Creation of operational efficiencies (35% of respondents)
• Reduced total cost of ownership (20% of respondents)
• Creation of a competitive advantage (17% of respondents)
• Yielded more accurate models (15% of respondents)
• Opened new business opportunities and revenue sources by providing greater access to new markets and customer segments (15% of respondents)

As for the top use cases for AI in financial services, the list is long — of the respondents, 20% or more leveraged AI for the use cases of natural language processing (NPL), recommendation systems, portfolio optimization, fraud detection, algorithmic trading, conversational AI, marketing optimization, and creation of synthetic data for modeling.

Overall, AI has proven to be a key resource and technology for any organization operating in financial and banking markets. With the right application and management of AI, financial service providers can leverage the technology to not only boost operational efficiency, but also to build deeper connections both with customers and within the industry itself.

Adoption of AI in Financial and Banking Markets

This versatility of AI has not gone unnoticed within the financial services industry, with many organizations striving to optimize their budgets and digital infrastructure to accommodate AI technology.

Yet, for a moment, it seemed as though AI adoption in the financial and banking industry had slowed — McKinsey reported in 2022 that AI usage had plateaued at between 50% to 60% over recent years for global organizations across all industries.

Pinpointing a specific reason for this adoption plateau can be difficult, as many factors have contributed to overall sentiments toward AI in financial services in recent years. What we can say for certain is that a plateau is far from a decline, and the steadiness with which organizations have maintained their existing AI strategies cannot be ignored.

Now, it seems as though AI adoption for financial services is finally picking up again.

In a 2022 market research report, the global AI-in-banking market is predicted to grow to a value of more than $64 billion by 2030, up from just $3.88 billion in 2020. As of 2020, roughly 32% of banks had already adopted AI to help build a more competitive advantage for their institutions.

3 AI Challenges for the Financial Services Industry in 2023

As we move into a more advanced era of AI, it is paramount for banks, financial service providers, and other financial institutions to have their heads in the game when it comes to AI adoption.

In many cases, the latest artificial intelligence trends overlap with numerous AI implementation challenges. Any new technological developments come with unique risks that must be addressed by both organizations and regulators before a new technology can be most effectively put to use.

However, like all financial innovation, where there are challenges there are also opportunities.

Let’s take a look at three key challenges AI poses for the financial services industry in 2023, and the underlying opportunities financial service providers should focus on:

1. Financial Accessibility & Inclusion

The first AI challenge for financial services to watch pertains to one of the technology’s biggest risks for the financial services industry — discriminatory business practices.

In the most basic terms, AI works by processing large quantities of data to not only generate insights, but also to actively learn and adapt based on its findings. While this is an exceptional capability, it comes with limits as well — should an AI system have limited or no access to quality data, the patterns and biases the system learns can be negatively impacted.

Just take a look at Deloitte’s take on unintended bias in AI:

When you contextualize this within AI-powered processes for banking, it becomes even more problematic.

For one, discriminatory banking goes directly against the expectations of Environmental, Social, & Governance (ESG) and can lead to compliance complications for financial organizations. Additionally, unintended biases in AI can significantly impact a bank or financial service provider’s ability to reach new customers, as it can greatly inhibit financial inclusion initiatives.

This makes training AI systems with quality data and building in the necessary controls to identify biases an absolute must when leveraging AI for financial services. To accomplish this, financial service providers will most likely need to engage in strategic partnerships with organizations specializing in the application of AI for financial activities.

Thankfully, advancements in AI have made the technology more ideal for optimizing and enhancing customer experiences, including by way of fostering greater financial inclusion.

2. Robotic Process Automation & Automation

Automation has always been one of AI’s top benefits for the banking and financial industry.

Specifically, robotic process automation (RPA) has shown tremendous value in helping to increase operational efficiency while lowering overhead costs for financial institutions.

In McKinsey’s 2023 report, Autonomy of AI: Staying on the forefront of AI in banking, it is revealed that the Chinese digital bank WeBank uses RPA to handle 98% of customer service requests through automated online channels. Additionally, WeBank states in the report that one staff member paired with RPA can result in 30% to 40% greater customer service efficiency.

The potential capabilities of AI for automation for finance are only widening — but where is the challenge?

For starters, integrating RPA into a digital infrastructure requires the relevant systems and digital resources to be more advanced than what traditional legacy systems can offer. As a result, organizations that are still relying on legacy tech may face a steeper curve when it comes to successfully integrating RPA into existing systems.

Additionally, RPA presents numerous security and compliance concerns that must be handled with the utmost care. To adhere to regulatory standards and keep customer information protected, financial service providers need to design RPA processes that understand and abide by the relevant regulations.

Like with the challenge of financial inclusion, overcoming the challenges associated with RPA comes down to the right strategic partnership. RPA and AI as a whole are complex technologies, made even more complex when legacy technology is involved.

With a reliable partner, financial service providers can leverage advanced RPA capabilities to drive operational efficiency and revenue over the coming years.

3. Generative AI

The rise in popularity of ChatGPT has opened up new conversation surrounding generative AI solutions—a type of AI that can produce text, audio, and visual content, as well as synthetic data for AI algorithm training.

Generative AI presents many interesting use cases for financial service providers, particularly by enhancing the customer experience via advanced GenAI chatbots. Compared to older chatbot models, generative AI for financial services can enable these programs to offer more tailored and personalized responses to customers, all while gathering data specific to an organization’s clients to learn and adapt from along the way.

According to an Insider Intelligence report from June 2023, generative AI has the “potential to revolutionize financial services” thanks to its many potential financial capabilities, including key use cases in personalized marketing and experience, process automation, fraud defense, risk assessment, customer success, and product development. Check out this article from Exadel to learn how your organization could get started with generative AI.

However, the report does go on to address the limitations of generative AI for financial services, stating:

Therein lies the key challenge of generative AI for financial services providers: as one of the newest AI technologies, generative AI solutions require further assessment from regulators and tech experts alike to determine the safest and most effective way to employ them.

Despite this, financial service providers should still be preparing now for the rise of generative AI. Those who begin strategizing now are the ones that will transform into AI-powered leaders down the road.

Building an Adaptable Financial Infrastructure with Exadel

To keep up with the rapid evolution of AI, financial service providers need trustworthy Fintech partners.

At Exadel, our financial service solutions encompass all of the latest technologies, including robotic process automation and predictive analytics. Plus, Exadel provides the consultative support and compliance expertise necessary to stay ahead of the regulatory curve for AI.

Get in touch with Exadel today to learn how our team can facilitate your AI initiatives.