AI Articles and Analysis about Data analysis

As AI becomes increasingly interwoven in the fabric of our everyday lives, the insurance sector is finding new business challenges and opportunities in all of the ways these emerging technologies bring increased risk - and security - to our lives.

As a business practice, model development aims to create a dataset, tailored through machine learning, that can accurately predict outcomes or classify data based on input variables. By following a structured approach, developers can ensure that the model development process is efficient, effective, and reproducible.

Market surveillance refers to activity authorities conduct to ensure that products available to consumers adhere to applicable laws and regulations. Additionally, market surveillance in banking and finance takes a somewhat specific form.

MetLife is a leading global insurance company headquartered in New York City. It provides its customers various insurance and financial services, including life insurance, health insurance, retirement plans, and investment management.

Although not often regarded as a technological first-mover, the insurance industry has recently seen robust, even rapid, adoption and deployment of AI capabilities, particularly in those related to natural language processing (NLP). 

A paradigm shift is happening in the manufacturing industry. Advancement in big data and machine learning is changing traditional manufacturing processes into the era of intelligent manufacturing. The concept of what gets called "industry 4.0" encourages the use of smart sensors, devices, and machines – going beyond the motives of collecting data about production. 

It is exceptionally difficult to get started, much less succeed, in AI adoption if one does not have at least a foundational education, particularly in those “modules” pertaining to AI capabilities, requirements, and organizational readiness. A business wanting to implement AI must understand the current state of adoption and how this current state matches up against AI readiness requirements.

Actionable, decision-augmenting data can be obtained internally or externally. Of course, external data is a far richer and more diverse source, as it comprises every other piece of digital information outside of the four walls of an enterprise.