Google the topic of artificial intelligence, and you’re likely to be taken down a deep, winding rabbit hole. If you venture only a little under the surface, you will encounter fantastical terms like perceptron, sigmoid neuron, and nonlinearly separable classifications. To save you from falling into that hole, this article will give a short, clear explanation of AI vs. generative AI.
Technology is an indispensable ally for navigating process compliance challenges with confidence. That’s because as digitization expands and regulatory requirements change, process compliance becomes more and more difficult to address manually. In this blog post, we’ll examine common process compliance challenges and how technology can help solve them. The most significant challenges for process compliance are manual and paper-based systems.
The effects of disconnected data are many: lack of data-driven decision making, inaccurate information, and slower processes, to name just a few. When you quantify the total cost of data silos, you’ll find that organizations have a lot to lose. While many data silo statistics predict a gloomy future where organizations struggle to unite their enterprise data, new approaches to data management, like data fabric, can help.
If your business has a lot of complex workflows, you’re likely relying on a number of tools and manual processes to get things done. And if you’re seeing that processes are slow, inefficient, or require a lot of manual work, business process modeling tools can help. Business process modeling tools are specifically designed to help you model and optimize your business processes.
If you’re not using artificial intelligence (AI) in your organization right now, you’re behind. But the reality is that beyond inputting some ideas into a large language model like ChatGPT, AI just isn’t that simple to operationalize across your business (although the benefits are real).
Why is private AI so hot right now? Organizations are reluctant to share their data with public cloud AI providers who might use it to train their own models. Private AI offers an alternative that lets them reap the transformative benefits of AI on process efficiency while maintaining ownership of their data.
The banking industry is no stranger to crises, and 2023 has proven to be a challenging time for banks worldwide. As financial institutions face a multitude of risks and uncertainties, robust risk management practices are essential for success. In this post, we’ll delve into the significance of risk management in weathering the 2023 crisis and offer key strategies for successfully navigating these turbulent times.
As artificial intelligence (AI) changes industries at a dizzyingly rapid pace, industries and governments alike are just beginning to grapple with the implications of the groundbreaking technology. One major issue has come to the foreground: data privacy concerns. Between possible data breaches and companies using your data to train their own models (and perhaps helping your competition in the process), enterprises have concerns.
Robotic process automation (RPA) refers to the use of software robots, or "bots," that mimic human actions to automate repetitive tasks within business processes. RPA technology can interact with applications, systems, and data sources to automate procurement tasks such as data entry, document processing, and report generation.
Bank turmoil associated with recent bank failures has highlighted the importance of due diligence for managing financial risk, growing revenue, and retaining jittery customers. Regulators will likely ramp up their scrutiny–even after hitting financial institutions with a jaw-dropping $2.7 billion in fines in 2021 for failing to meet compliance and due diligence standards.
