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.
Your organization has an unstoppable digital transformation plan—at least that’s what you thought before you started encountering the disadvantages of data silos. Your teams were fired up and ready to go, but data separated by different systems created obstacle after obstacle to your digital transformation dreams.
The insurance industry is quickly moving into a digital-first future where emerging technologies will reshape the way policies are sold, claims are processed, and risks are managed, and where market leaders are automating critical workflows to deliver seamless experiences, enhance customer satisfaction, and unlock profitability.
If you’re worried about the potential for heightened regulatory scrutiny in financial services, you’re not alone. Business operations teams everywhere are focused on the end-to-end, client lifecycle management (CLM) process as they cope with ever-changing regulations governing how, when, and where client data can be stored and accessed. It’s hard to stay compliant when customer data is spread across multiple operational silos.
