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.

Imagine a multinational business that deals with multiple suppliers and customers scattered across the globe, each with their own systems and processes. How does it ensure smooth communication and transactions with its business partners? The answer is Electronic Data Interchange (EDI), a standardized method for exchanging electronic documents between businesses.

For life sciences organizations, the key to success is the ability to bring the highest quality products to market quickly and cost-effectively. However, the commercialization process is filled with hurdles that hinder time to market, such as complex regulatory requirements and managing cross-functional collaboration. These challenges require a strategic and well-coordinated approach for successful commercialization.

Data elements are the fundamental building blocks of EDI documents. They represent individual information within a transaction set, such as city, state, country, item number, quantity, and price. Each data element is defined by its data type, which specifies whether it’s numeric, alphanumeric, a date, or a time. Additionally, the definition includes details like minimum and maximum length requirements and any applicable code values.

What is data fabric architecture? The case for data fabric 4 key facts about data fabric architecture Data fabric architecture can modernize and future-proof your data approach Data fabric architecture is a virtual data layer that connects any number of external systems together to provide unified access and a complete, 360-degree view of an organization’s data. Whether your data lives in legacy systems or multi-cloud environments, a data fabric can connect these systems together for a unified view.