AI is the perfect tool for banks to remain competitive with fintechs

Banks are becoming outdated today, especially with the rapid rise of the fintech sector that aims to provide a more efficient, cheaper, and user-centric alternative to conventional financial services. 

Based on
Statista’s data, neobanks in Europe had an 11.1% market share in the banking industry, while their US-based counterparts accounted for 15.5% of all bank accounts in 2023. With total neobanking transactional value
projected to surge from 2024’s $6.37 trillion to $10.44 trillion by 2028 at a 13.15% CAGR, these fintech startups represent a significant threat to traditional banks.

At the same time, banks face numerous challenges that could further weaken their competitiveness. Stricter regulation and a lack of automation present significant problems, and financial institutions must embrace new technologies to solve them.

Manual work and regulatory changes put a heavy toll on banks

Following last year’s bank failures, regulators aim to introduce stricter measures for financial institutions to prevent banking collapses
and protect consumers. An example of this is the Basel III Endgame, a final set of measures proposed by the Basel Committee to enhance financial institutions’
regulation, risk management, and supervision.

With more regulations and stricter rules, it becomes more challenging and expensive for banks to fulfill regulators’ requirements. They have to employ high-priced specialists and dedicate additional human resources to compliance, an activity that banks’
customer onboarding teams already spend 91% of their time on alongside operational tasks.

Additionally, the lack of automation in areas like customer service and credit scoring results in significant manual work for banks. This requires many employees and increases the institution’s expenses.

To stay relevant and competitive with fintechs, banks need to move away from their historically cautious approach and embrace new technologies like AI. In fact, rescind data showed that the use of artificial intelligence
could boost banking sector revenues by up to $1 trillion by 2030.

So, how can banks leverage AI in their technological evolution?

Supercharged efficiency at decreased operational costs

Banks should explore AI’s potential use cases for AML compliance and fraud detection.

Today, AML compliance requires strict adherence to procedures and pattern recognition, a task that is routine and needs constant attention. And current methods, like transaction monitoring systems, are resource-heavy and inefficient, often leading to numerous
false positive alerts. 

AI can cope with AML compliance and fraud detection much more effectively than humans at much lower operational costs and with more rapid response times. Combined with machine learning, artificial intelligence tools can continuously learn and find new, more
capable ways to detect violations.

Contrary to popular belief, using AI and ML tools for such tasks does not eliminate the need for a human to verify the final stage. In fact, regulators mandate a compliance officer to make the financial decision in these cases.

Coming into contrast with popular belief, it must be noted that implementing AI tools into banks’ processes will not replace employees. Instead, they will assist them with their professional tasks to enhance their productivity. Artificial intelligence will
perform the most resource-intensive part of a process, with a human worker reviewing and finalizing it at the end.

Moreover, banks can utilize AI to enhance efficiency and reduce the costs of their customer support and risk analysis teams. Also, large language models can offer a solution to the subpar services offered by traditional rule-based chatbots. They can interact
with customers more quickly and with tailored messages, adapt to each user, operate 24/7, and continually learn to enhance the quality of communication. For example,

McKinsey has developed a virtual AI expert that can provide personalized answers based on proprietary information and company assets.

The same applies to customer risk assessment and credit scoring. Based on the available historical data, generative AI will make a more accurate assessment of the client according to the risk model. In the end, it will perform such tasks in seconds rather
than, as is currently often the case, in days.

The next big AI banking trends of the future

In the coming years, AI is expected to become widely adopted by financial institutions. During this time, most banks will aim to automate all routine banking processes using AI. Currently, financial institutions
allocate between 60% and 80% of their payrolls or more to positions that are likely to be affected by generative AI.

For that reason, a strong reduction in the lower level of bank employees will occur, enabling banks to cut their operational expenses significantly.  The remaining professionals will be those most capable of leveraging AI to enhance their work and complete
processes like AML compliance and fraud detection.

With the implementation of AI, banks will become more effective in combating money laundering and fraud. Additionally, the use of generative AI in customer support will provide a more personalized approach, creating an experience tailored to each client’s
needs and preferences.

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