The Future of AI in the Banking Industry: How AI and Automation are Changing the Game

McKinsey recently conducted an in-depth study of the current state of the banking industry and its approach to AI and automation, surveying key industry players about the state of their organizations' AI strategies and integration. Their findings reveal that banks leveraging AI at scale can achieve up to a 30-50% acceleration in development timelines by reusing technology and code, while also unlocking $1 trillion in annual value globally.

This research could not have come at a more opportune time, as the banking industry faces growing pressures to modernize and fully embrace AI and automation in their business processes. In fact, according to a study by HFS and Infosys, this year’s AI investments are expected to represent 16% of the total technology spending, with a 25% increase across the entire banking industry. This is due to the newfound expectations of customers, concerns that competitors will outpace them, and worries about cybersecurity, as bad actors increasingly embrace these new technologies.

In a fiercely competitive industry, with banks and financial firms pouring millions into AI and automation investments, adopters cannot afford to simply experiment casually. They must craft a robust strategy—one that helps them integrate, onboard, and upskill with this new technology quickly and effectively.

So, what does an AI-first bank look like? This article outlines key industry survey findings and explores forward-thinking approaches to AI and automation in banking. Read on to learn more.

The 3 Pillars of AI and Automation in the Banking Industry

Implementing advanced automation technologies (Generative AI, Agentic AI, Robotic Process Automation, and more) is already a high priority for the majority of small and large-scale banks. Indeed, a recent Gartner study reveals that 80% of banks plan on adopting some form of Generative AI solution by 2026.

But what specific use cases are the majority of banking firms interested in, and where do they plan on integrating this tech in the near future? In our latest eBook, we explored these questions in greater detail, and based on our own experience of the subject, we see the main business applications of AI in banking as comprising three essential pillars:

1. Customer expectations and customer experience
2. Transformation numérique et efficacité
3. Gestion des risques et conformité

Let’s break each of these pillars down further and explain how AI and automation applies to each.

1. Customer Expectations and Experience

AI boasts a number of useful applications when it comes to improving the overall banking customer experience and meeting customer expectations.

In fact, a recent Everest Group study found that 39% of enterprise executives noted a clear improvement to customer experience through unified intelligent automation solutions.

For example, Krungsri Consumer wanted to improve their customer service offerings and support. With the help of AI and automation, they were able to eliminate manual customer support processes enhancing efficiency and quality to achieve an 85% productivity boost in speed and responsiveness.

2. Digital Transformation and Efficiency

AI can integrate with a bank’s existing IT system to automate manual, error-prone and repetitive tasks, such as data entry, thereby improving operational efficiency, quality and productivity. This enables banks to reduce their overhead costs and accelerate the time-to-market for new products and services.

Furthermore, AI can analyze vast datasets to uncover deep business insights, driving better decision-making and improved financial outcomes. For example, ABN AMRO (a leading Dutch Bank) deployed AI-driven document scanning and ML models to achieve up to a 90% reduction in time and cost for issuing credit cards and mortgages, and a 30% reduction in dedicated time for fraud detection processes.

3. Risk Management and Compliance

According to the recent Avande AI Readiness Report, 41% of banking professionals cite automation of risk, regulation, and compliance process handling among the most exciting use cases for AI, highlighting its potential to ensure safe and responsible transactions.

We recently assisted a major European bank in replacing its manual sanctions screening with a streamlined digital workflow. As a result, the bank achieved an 80% reduction in claims processing times while avoiding a 15% increase in trade finance headcount.

What’s Next For AI and Automation in Banking

McKinsey analysts suggest that for banks to maximize the benefits of AI, they must move beyond fragmented pilot projects and adopt a holistic, enterprise-wide strategy that aligns with their overarching business objectives.

This transformation requires significant effort, fostering a pro-AI culture, modernizing data and technology infrastructure, and addressing risks such as bias, privacy, and security to ensure compliance with evolving regulations.

This is where solutions like Tungsten TotalAgility can make all the difference.

Tungsten TotalAgility: Your Partner in Realizing AI’s Full Potential

TotalAgility is an AI-driven intelligent automation solution that blends advanced document processing, workflow automation, and knowledge discovery capabilities to help businesses streamline and automate complex, content-heavy workflows.

With over 1,800 global banking partners, including 8 out of 10 global banks across 76 countries, Tungsten is ideally placed to help your bank achieve its automation goals.

So, if you’re ready to take your first step forward toward AI-first banking, download our exclusive Bank on AI eBook here to see more use cases, and get in touch for a free TotalAgility demo today.