Banking automation has become a key driver of efficiency and innovation in the financial services industry. As the banking sector strives to meet the demands of customers while managing costs, they’re turning to technology to streamline operations. But what exactly is banking automation, and how does it work? Let’s break it down. Banking automation refers to the use of technology by financial institutions to perform tasks that would traditionally require human intervention.

Companies use a wide range of both artificial intelligence (AI) and automation tools, and each automation tool serves a different purpose, often working together to boost efficiency. In this blog, we’ll explore the differences between AI and automation, how they can complement each other through intelligent automation, and five real-world examples of how they work together. We’ll also highlight the benefits of using AI in business process automation.

Enterprises have complex IT and technology needs. To reach business goals, organizations must often stitch together a patchwork of software and IT systems. So, it’s understandable that the thought of adding yet another technology—low-code—might cause trepidation. But low-code excels at integrating seamlessly with any technology. Low-code tools speed up development work and enable rapid application development.

For insurance companies, balancing customer expectations with the rigorous requirements necessary to mitigate risks poses a challenge. Especially when you’re using manual underwriting processes. By turning to artificial intelligence (AI) in insurance underwriting, you avoid costly delays, streamline your employees’ processes, improve accuracy, and create an optimal customer experience. In this blog post, you’ll learn how AI facilitates greater efficiency in underwriting.

In the world of banking, challenges abound. Fragmented processes and add-on technologies that don’t integrate well with legacy equipment pose issues for financial institutions already struggling with ever-increasing regulatory compliance requirements and customer expectations. Banks have invested heavily in anti-money laundering (AML) solutions to keep up with heightened risks and remain competitive.

What are the differences between generative AI vs. large language models? How are these two buzzworthy technologies related? In this article, we’ll explore their connection. To help explain the concept, I asked ChatGPT to give me some analogies comparing generative AI to large language models (LLMs), and as the stand-in for generative AI, ChatGPT tried to take all the personality for itself.

The human resources function plays a critical role in ensuring federal government agencies have the right talent and resources in place to meet their missions and serve the public effectively. As workforce needs become more dynamic, HR is expected to drive organizational change, streamline processes, and promote a culture of innovation and accountability, ultimately helping agencies succeed in their broader objectives.

From sourcing raw materials to delivering finished goods, the pharmaceutical supply chain involves many complex steps, each of which must be carefully managed to maintain product safety and compliance with strict regulations. Improving logistics processes helps ensure speed and quality across the supply chain, leading to better outcomes for both the industry and patients alike.

Insurance companies have long relied on data to assess risk, underwrite policies, and handle claims. However, as the volume of data grows, so does the need for sophisticated tools to turn that information into actionable insights. Simply having access to data is no longer enough. To succeed, insurers must be able to harness the power of their data—transforming it into a strategic asset that drives both operational efficiency and improved customer outcomes.