AI in Banking: Perks and Implementation Challenges

Artificial intelligence is a promising technology in banking, with the potential to significantly enhance operations, yet it’s challenging to implement in this highly regulated sector. In this article, we’ll provide an overview of how AI is helping financial institutions to thrive and outline the main challenges in its adoption.

Why do banks need AI?

Financial institutions that have implemented AI-powered software have reported significant improvement in various areas of work. Thus, NatWest Group, a major UK-based financial powerhouse, managed to decrease its fraud incidence rate by an impressive 6%, including a 90% drop in fraud during the account registration process. At the same time, the holding saw a fivefold increase in click-through rates for customized lending via tailored offers.

Brighterion AI by Mastercard is another example of a powerful AI model for financial institutions that helps them analyze transactional data for fraud patterns and supports 150 billion transactions annually.

UK-headquartered banks that rely on AI have introduced algorithm-driven loan assessments for amounts under $100,000. Giants like JPMorgan Chase have built dedicated in-house AI models to peruse the statements of the US central bank and capture underlying policy signals. Goldman Sachs, in turn, is setting the tone in the use of AI to generate code and draw up documents. Finally, Morgan Stanley employs machine learning to pinpoint tailored investment opportunities and provide next-best-action recommendations.

In investment banking, the prospects are bright as well. According to Deloitte, the fourteen largest banks have the potential to improve their front-office performance by 27%–35% and generate an incremental $3.5 million in revenue per employee by next year solely by relying on generative AI. For example, AI can effectively enhance the process flow of underwriting submissions associated with customized policies.

Practical tips on AI implementation

The primary reason banks are rapidly adopting AI is to free up valuable resources for product design, testing, development, and maintenance. Currently, a substantial part of work in banking — whether in the office, when designing and testing new features and offers, or handling contact center requests — is extremely repetitive. By adopting AI, banks can free up resources for more sophisticated and complex tasks.

How should banks aiming to embed AI into their workflows and products proceed?

Below are several tips from trusted software experts:

Firstly, rethink your business strategies to identify high-impact areas, e.g. customer service, risk management, etc., where digitalization will bring the most benefits. If needed, reach out to a dependable IT company for an audit of your current infrastructure and AI consulting. This developer can further help you carry out AI development and implementation end-to-end.

Secondly, mind the risks that come with AI adoption, e.g. the security and confidentiality of clients’ data and compliance with existing financial regulations.

Finally, invest in both training your staff and educating your clients on the newly implemented features such as chatbots, robo-advisors, and more.

The AI-driven future of banking

So, what are the key future trends in mobile banking app development? We believe that in the next five years, the following trends will gain momentum:

• The evolution of super apps, i.e. mobile banking platforms that will function as all-in-one infrastructures, integrating various public services.
• Advanced personal advisors powered by an AI engine that will help clients manage finances, compare and negotiate the prices of goods, and more.
• AI usage for complex products that will customize the content and communication with clients, ensuring that offers are highly relevant to each client’s specific needs.

Why AI adoption in traditional banking will take time

Using AI solutions for end-user engagement in banking offers many perks, including enhanced API usage, streamlined customer service, smart credit scoring, reinforced cybersecurity, and more. However, integrating AI into banks’ products and operations is a long-term project. The main hindrance is stringent industry regulations that prohibit using client data in open AI models. The solution? Banks can build their own models in-house and analyze data internally. All the same, banks must obtain client approval, and the proper training of data models will require substantial time.