The recent failures of regional banks in the US, such as Silicon Valley Bank (SVB), Silvergate, Signature, and First Republic, were caused by multiple factors. To ensure the stability of the US financial system, the implementation of advanced liquidity risk models and stress testing using (MI/AI) could potentially serve as a protective measure.
A few weeks ago, I wrote a post summarizing "Seven Data Integration and Quality Scenarios for Qlik | Talend," but ever since, folks have asked if I could explain a little deeper. I'm always happy to oblige my reader (you know who you are), so let's start with the first scenario: Database-to-database Synchronization.
Greg Peranich and Tryn Brown contributed to this post. The world of financial services is driven by digital experiences. Over the last 20 years, virtually all banking activities have been taken online (an unfortunate change only for fans of pneumatic tubes and pens on beaded chains). Like other industries that have undergone digital transformation, financial institutions are leveraging APIs to give users a more seamless experience when managing their money.
Let’s talk about the world’s currently trending topic for a second: AI. Now, before you click out of this blog, sighing to yourself that this is yet another blog that wants to tell you how to write code with ChatGPT; bear with us. As almost everyone has used some form of AI – especially ChatGpt – to help them with some form of a task, we can all agree that it’s quite an interactive experience.
