Artificial intelligence (AI) is set to transform the banking industry as we know it, and top management in the sector must prepare for this period of disruption before it is too late. If they do, the rewards are significant with industry leaders already predicting that AI could boost revenues by almost $1 trillion by 2030[1].

While banks are currently in the early stages of adopting generative AI, focusing on pilot projects and other automated processes to improve efficiency, these innovations will have a much broader and more considerable impact on the sector moving forward.

For example, AI has already shown its ability to generate loan recommendations or guide customers through the process of choosing an investment product without the need for human input. Likewise, in investment banking, AI can support pitching teams, identify M&A targets, or provide predictive risk scoring models to automate some of the work of risk management teams.

It’s clear that the technology is starting to play a central role in this space, but some European banks remain behind the curve and are still somewhat unprepared to take advantage of the technology. Kearney’s recent study with Egon Zehnder revealed that 73% of business leaders are ill-equipped for AI transformation, despite 85% of leaders seeing the technology as an opportunity.

Research has also revealed that European banks seem to be lagging their American counterparts when it comes to AI. In fact, the top five North American banks are responsible for more than 67% of AI research publications within the industry, have filed 94% of the patents related to AI, and are responsible for more than half (51%) of the investments in this field[2]. This issue must be addressed if European banks are to capitalise on the vast array of opportunities for value creation that AI offers.

Generating value from AI

One of the major benefits of AI in banking is its ability to help personalise the banking experience for customers, ultimately unlocking greater value, extending the customer life cycle and limiting churn. Already, AI solutions can open new accounts, assist with customer onboarding and provide personal recommendations for loan solutions, all of which help improve customer satisfaction. Moving one step beyond this, these solutions can also collect and analyse data to uncover customers’ unmet needs which can significantly increase the value proposition for the customer.

AI solutions can also identify key life moments for B2B cases that extend the customer life cycle. This could range from hiring new employees or launching social medial campaigns. Equally, the same could be applied for B2C cases, such as buying a new home. Banks can then use this information to adapt their offers and communications with this in mind. Through enhanced data collection using AI, banks can easily identify patterns indicative of potential churn, allowing them to proactively address at-risk customers and retain clients.

Tech for mitigating risks and lowering costs

AI solutions clearly have the ability to create a better value proposition for customers. However, for leaders in the banking sector, the challenge lies in harnessing the technology to not only manage risks but also materially reduce existing costs to improve their bottom line.

With AI likely to directly impact half of the tasks being done by 15% of banking industry workers, according to the Organisation for Economic Co-operation and Development, AI solutions will greatly increase the productivity of employees, and reduce costs across banking businesses. The knock-on effect is that employees will be able to spend more time on tasks that benefit from human input and add more value to the business.

To reap the full benefits of AI, leaders must start developing a strategy for adoption now. Employees will take time to adjust to the cultural and operational changes that come with AI, and business leaders will need to develop an evolving AI strategy that enables employees to learn how to work effectively alongside these solutions.

Costs can also be reduced by using AI to mitigate risk. For example, AI can detect fraudulent transactions earlier using computer vision and pattern recognition on customer transactions. A leading bank for Europe, the Middle East, and Africa has implemented solutions to prevent cross-channel fraud, including a data science-based payment fraud prevention solution that relies on AI and machine learning capabilities to help banks intercept fraudulent activities upstream. The innovation will alert banks about emerging fraud threats so that they can react in a timely manner. 70% of businesses report that fraud losses have increased in recent years, so the growth of AI could not have come at a better time to reverse this trend.

Another major challenge for banks is dealing with customer complaints. However, AI-enabled chatbots can help here, too. Using chatbots and call center analysis software, banks can address and resolve issues that their customers are facing more effectively. Technology such as voice and text analytics will allow for a better understanding of why complaints are happening, which in turn can reduce call the volume of calls for banks and their support teams.

If banks can effectively harness the power of AI to address the issues above, their costs can be dramatically reduced across several business areas. Those who fail to do so, risk falling behind and will struggle to compete with their peers. While the scale of changes can seem dramatic, driving smaller, manageable initiatives with tangible results can build a foundation for strong and lasting transformation.

Huge value potential, but not a simple process

AI has the potential to transform the entire banking value chain by improving the customer experience, increasing value and optimizing costs structures. That being said, various conditions must be met first. AI technologies depend on diverse data sources (internal or external), meaning data cleaning will be essential. Equally important is to have robust digital, IT, and data architectures that are effective in supporting AI and GenAI solutions.

Banks must also pay close attention to the regulatory requirements around AI implementation. European banks especially will be obligated to comply with the AI Act set up by the European Union in December 2023 to better govern AI systems. Among other things, this means that high-risk use cases of AI, such as assessing creditworthiness with AI, will have to comply with significantly heightened regulatory requirements.  

Prioritizing the effective delivery of AI systems will ensure that banks are on the right path to streamline their operations, while optimizing costs as much as possible. Banking management teams must be identifying how they can best use the technology to create a viable ecosystem and improve their business model, while remaining compliant with regional regulations. Banks that overcome these challenges will be industry leaders in AI adoption, ahead of huge transformations for the industry.

[1] https://www.consultancy.uk/news/36741/ai-in-banking-could-yield-1-trillion-revenue-improvements#:~:text=Artificial%20intelligence%20could%20boost%20revenues,banks%20to%20feel%20that%20benefit.

[2] Evident study, 2023