ChatGPT has taken the world by storm. Apps powered by large language models (LLMs) have suddenly taken center stage, with OpenAI, Google, Baidu, and Microsoft getting into the act. A spurt of startups and use cases has emerged on the scene. Suddenly, Generative AI, a branch to which these LLMs belong, seems to dominate every conversation, not just in the technology industry but among professionals the world over.
The insurance industry has always been one of the most data-intensive industries. With a large amount of customer data, claims data, and other information, insurers have been adopting artificial intelligence (AI) to streamline their processes and provide better customer experiences. AI-powered technologies have enabled insurers to automate claims processing, fraud detection, and underwriting, among other things. Here are six top use cases of AI for insurance.
A hardware security module (HSM) is a tamper-resistant physical computing device that safeguards and manages digital keys, performs encryption and decryption functions for digital signatures, and provides strong authentication and other cryptographic functions. HSM has mature technology, with high availability, scalability, and usability. These modules traditionally come in the form of a plug-in card or an external device that attaches directly to a computer or a network server.
ChatGPT has taken the world by storm. A simple interface is now being used by everyone and for everything. If you are in IT, I am sure not just your colleagues, but folks from your family that do not belong to the IT world are also talking about it. ChatGPT registered 13 million users per day in January. In comparison, it took Instagram and TikTok 2.5 years and 9 months, respectively, to reach 100 million users on their respective platforms.
The Internet of Things (IoT) is expanding at an unprecedented pace. As IoT solutions become more complex, the need for comprehensive and efficient IoT testing increases.
In today’s dynamic business environment, enterprises realize the importance of a quality-first approach to test automation. Prioritizing quality at speed over a faster release pipeline is essential for ensuring the success of an organization. However, implementing a successful quality-first approach requires an end-to-end collaboration, where quality becomes the responsibility of all the different personas involved in the software development life cycle (SDLC).
Almost every organization needs a top-notch monitoring system to make sure everything is in place and operating at its best, given the developments in the cloud and changing software development life cycle (SDLC) processes. Observability is one of the significant areas that can guarantee your system’s health and ensures that the performance is visible and monitored properly.
