AI in Banking: Ushering a New Era of Engineering Productivity

AI is revolutionizing the banking sector by significantly enhancing software engineering productivity. Through automation, intelligent code generation, and improved development workflows, banks are accelerating innovation, reducing time-to-market, and optimizing resources. This new era empowers engineering teams to focus on high-impact tasks while AI handles repetitive processes, leading to more efficient, secure, and scalable digital banking solutions. As a result, financial institutions are better equipped to meet evolving customer demands and stay competitive in a rapidly changing landscape.

Banks that adopt AI tools effectively throughout their software development processes may achieve notable cost reductions by 2028. In recent years, AI has begun reshaping software engineering far beyond previous expectations. Today, a large majority of developers around 84% are already leveraging coding assistants powered by large language models. Many banks are now providing their engineering teams with advanced AI tools to enhance productivity.

The advantages of this shift are significant. According to Deloitte’s FSI Predictions 2025 report, AI could lead to software investment savings of between 20% and 40% for banks by 2028. For individual engineers, this might translate to savings of $0.5 million to $1.1 million during that time.

Large language models have proven especially useful in speeding up software development, with reported productivity improvements of up to 55%. These gains extend across the entire software lifecycle. In the early stages, AI can assist with analyzing and categorizing requirements and identifying hidden needs to better define project scopes.

Additionally, AI’s low-code and no-code features are already shortening development timelines. During design and testing, engineers can use natural language inputs to generate initial design concepts and evaluate trade-offs. AI testing tools are also becoming smarter by learning from historical data to enhance future test performance. Some AI systems are capable of automating deployment tasks, ensuring seamless rollouts and reduced downtime.

Together, these capabilities lead to faster insights into business requirements, better system designs, stronger testing processes, predictive maintenance, and greater software reliability.

Despite employing around 100,000 software professionals across the U.S., many banks struggle with inefficient development practices. Larger institutions sometimes allocate 15% to 25% of their workforce to tech roles, yet still face challenges like slow runtimes, high maintenance costs, and integration issues.

AI offers a solution. For example, generative AI models are now being trained to modernize legacy mainframe code from the 1960s. With more banks experimenting with low-code and no-code tools, development speeds are set to increase further.

As AI capabilities mature, banks will likely see clearer business value. New AI agents are emerging that can turn natural language instructions into functioning code. Some of these newer models are more affordable and resource-efficient, enabling even wider adoption.

To maximize the impact of AI, software leaders should begin by targeting the most inefficient areas in their development cycles. Transparent communication with engineers about changing roles, skill-building, and future career paths is key to successful implementation. Establishing strong governance frameworks is also essential to address risks such as data security and algorithmic bias.

Banks should also consider working with external vendors, particularly if internal capabilities are limited. Keeping stakeholders informed about progress and responsible AI integration measures helps build trust. Lastly, fostering collaboration between different departments will support more effective and cohesive adoption across the organization.

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Source: WSJ‍