What to expect in the next era of artificial intelligence in banking

The rapid evolution of artificial intelligence (AI) has the potential to radically reshape how banks operate from front to back. This wave of artificial intelligence will have a lasting impact on employees, customers, and regulators as it becomes more ubiquitous. Banks will need to navigate technology and organizational change with a renewed emphasis on collaboration in order to execute on their AI strategy.

Generative AI is a potent new tool in the bank’s arsenal. It can take on more of the burden for customer servicing and reduce the toil of back office operations. In the short term, the positive impact will be on the bottom line, but we believe that this next era of artificial intelligence will be critical to value creation for banks and undoubtedly shift the competitive landscape. While the opportunities are vast, there are many challenges that banks will need to address to maximize AI’s potential.

The future of artificial intelligence in banking

Banks have a long history of using predictive AI to automate and streamline operations within the bank. For example, using patterns to reconcile payments or assist debt collection by predicting who is the most likely to repay. However, there is a significant opportunity to expand the use of AI to other areas of the bank to boost sales, manage risk and optimize operations as we look to the future of banking.

From customer acquisition and onboarding to advisory, banks have the opportunity to enhance how they are reaching and interacting with potential customers along with creating new value streams. There are many ways that AI can enhance customer satisfaction and retention, while making it faster to acquire and onboard new customers. AI can help identify potential clients more efficiently by using predictive analytics, customer onboarding can be fully automated and create an enhanced customer experience with more personalized products and services.

AI can also help banks’ operations and servicing teams when used to boost processing and support, reducing wait times and improving operational efficiency. Financial advice could be more intelligent and adaptive to changing conditions, exception handling in banking could be sped up and AI-powered assistants could handle more complex customer inquiries and problems in a more conversational and less robotic tone. Additionally, financial reporting in banking could be streamlined with the use of AI, automating data compilation and analysis for more accurate and timely reports.

AI will  play a significant role in a bank’s ability to keep pace with market change. With the ability to analyze large data sets, risk modeling in banking can be much more robust and dynamic to predict and mitigate market risks more accurately. Furthermore, AI could better detect financial crime by using sophisticated pattern recognition to identify suspicious transactions and reduce false positives.

Challenges of scaling AI across the bank

While the future of AI in banking looks promising, scaling AI adoption won’t come without challenges. Adopting AI technology involves technical adjustments as well as shifts in customer expectations and organizational practices. As banks consider deeper integration inside the organization, it's important to recognize the hurdles that may arise and be prepared to overcome them.

Many obstacles are likely to present themselves on the path to expanding AI to new areas related to product, data, compliance, operations and talent acquisition and training. Expanding AI adoption throughout a banking organization, across delivery teams and operations is a significant challenge, especially when the pace of change continues to increase. Making AI more approachable to these groups with the tools they need will be key to deepening its impact. Scaling AI will require a platform that brings these teams together with the tools they need.

Banks also have to navigate a plethora of other issues like convincing customers who are untrusting of AI to use AI-based services, data privacy and security, as well as hiring and retaining AI professionals who are skilled in both data science and the banking business. Though these issues may seem like a big undertaking, understanding the necessary capabilities and finding the right partners and tools to facilitate the integration of AI makes all the difference.

Trustworthiness will be key

Using AI in new areas of the bank can raise new concerns about privacy, accuracy and fairness. This will require bolstering how data is sourced and models are managed, so that clients and regulators can better understand how AI is being used. Monitoring for model bias and drift is a key capability to ensure that banks continuously assess and adjust their AI models to prevent inaccuracies and biases. Regular audits and reports to regulators are necessary to maintain compliance and transparency in AI usage. For example, our integration with watsonx.governance enables banks to effectively manage model risk.

Red Hat is helping banks across the globe scale AI adoption, helping them get more value out of AI without leaving their investments stranded. Red Hat® OpenShift® AI gives teams the ability to train, tune and serve models across any cloud. It provides a modern platform for bringing data scientists together with developers to scale artificial intelligence across the organization.

By fostering strong partnerships and offering adaptable solutions, Red Hat helps financial institutions navigate the complexities of AI adoption. Learn more about how Red Hat technology can help you expand AI to new areas of the bank.