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Stay Ahead of the Curve: Top 3 Banking Technology Trends to Watch in 2024

Michael Heffner, Vice President, Solutions and Industry Go To Market
November 7, 2023

Did you make it to this year’s Sibos conference in Toronto? As the world’s premier financial services event, Sibos is one of the best places to take in advice and predictions from expert—even prescient—leaders in the industry. So, what did some of the most trusted voices in finserv have to say about what we should expect in 2024? Here are the highlights.

The combination of high interest rates, high inflation, and increased economic uncertainty means banking leaders in the financial services industry have had to narrow their focus to their most core concerns. Many are aiming to improve operational cost effectiveness, reduce risk when it comes to regulatory compliance, and keep their customers happy with excellent service. 

Technological advancements will have a big impact on the future of banking and the banking landscape as a whole. Banks that adopt artificial intelligence (AI) and other advanced technologies will be able to adapt to changes in the financial industry more easily than those that dismiss them as optional. 

Here are three key banking technology trends covered at this year's Sibos conference that are on the rise in the financial services industry.

Trend 1: AI will continue to change banking technology for years to come.

It may seem like artificial intelligence has become the hot topic of the day, but it's not a trend that's going away any time soon. AI has the power to transform the banking industry when it comes to risk management, operational efficiency, customer experience, and more. Digital transformation with a good AI strategy will equip financial services organizations that embrace it to become more agile as the financial landscape changes.

AI use cases in the financial services sector.

Regulatory risk and compliance: Artificial intelligence can discern patterns and behavior to identify risks early. By analyzing historical data and predicting future scenarios, banks can assess market risk, credit risk, and operational risk and make their risk mitigation efforts more effective.

Customer service: Customer satisfaction and customer retention in the banking industry are of utmost concern. When you combine AI technology like chatbots with employees working to solve the critical problems that customers face, you're able to improve outcomes and better engage people with personalized experiences. Additionally, AI customer service offerings provide further data analytics on customer behavior, which improves service offerings and marketing efforts.

Operational efficiency: AI can automate mundane, routine tasks to help save time and create operational efficiencies. It has the ability to analyze data and information faster and more accurately than humans can, which improves the visibility within an organization so leaders can make better decisions, faster. 

[The four pillars of AI in financial services are predictive AI, anomaly detection AI, classification AI, and generative AI. Learn how they make an impact.]

Trend 2: Data is king and key to making the most of AI technology.

At Sibos 2023, it was clear generative AI had captured the attention of financial services leaders. But amidst the buzz, it's easy to forget that AI is nothing without good data

One of the top trends we saw at the conference was a focus on that data. Many traditional banks and financial institutions are still using spreadsheets created and maintained by humans, which increases the potential for human error and risk.

Connect your data.

Siloed data results in a narrow perspective and an incomplete view. Wherever possible, connect data across disparate systems to create a unified view and harness its full potential. This not only enhances AI automation, but it also ensures everyone who needs to access it within your organization has the most accurate information. For large banks, this has been a challenge. Technology that makes use of data fabric can help. A data fabric helps you work with data in a virtual architecture so you don't have to migrate it from one platform to another to use it. With a data fabric, it’s as if all your data is connected, no matter where it lives.

Maintain the integrity of your data.

If your data isn't of good quality—meaning as complete and accurate as possible—the technology that relies on it won't work. Bad data can also lead to bad business decisions, regulatory fines, and customer dissatisfaction. Improve the accuracy of your data by involving IT teams in the process of defining, standardizing, and otherwise handling it. Look for places where there's friction in your data entry processes and work to improve those workflows to enhance the integrity of your data.

Stay alert to AI and data privacy concerns.

Leaders are right to be concerned about privacy when it comes to data and AI. Information that's fed into the language models of many AI products is used to train the model for future output. If you feed it proprietary information or sensitive customer data, that information may be exposed publicly, creating additional risk for companies regarding ownership rights and regulatory concerns.

The solution to this in the financial sector is private AI. With private AI, the language model is internal to your company and is only trained on your own data. This provides you with the benefits of AI while maintaining a high level of security for your organization and customers. It also means the AI outputs specifically reflect your customer base, giving you better insight into the needs and habits of those you serve.

[View our checklist on implementing AI in your organization.]

Trend 3: Digital assets and tokenization are on the rise. Automation can help.

Most large asset manager banks and financial organizations are exploring digital assets, tokenization, and blockchain technology. The digitization of these assets will bring more real-world assets to a wider range of potential customers and allow money to move more easily around the world securely. 

A growing number of investors are interested in investing in these new assets for wealth management, but traditional business models don't always make that possible. Fintech companies and modern banks are leading the way in addressing pain points and solving these data challenges. 

How are modern banks doing it? AI automation. Many of the tasks associated with digital assets can be facilitated using automation, like assessing the value of assets, financial forecasting, and more. AI can also be used for the risk assessment, risk management, and regulatory compliance of these financial products.


Future-proof your organization with AI-powered tools.

Financial services companies are facing massive change within the industry. Banking leaders need to be aware of how technology can help them adapt to save on operating costs, improve banking processes and digital experiences for customers, and reduce risk. A strong AI strategy combined with data and process automation is how you’ll succeed in 2024.

Read about top AI use cases in financial services, including in risk, compliance, and beyond, and how to navigate AI risks.