AI Articles and Analysis about Customer analytics

Retail fraud and loss prevention have always been significant business concerns impacting profitability and customer trust. However, with the emergence of AI technologies, there is a newfound potential to combat these challenges more effectively. 

This article is sponsored by Riskified, and was written, edited and published in alignment with our transparent Emerj sponsored content guidelines.

The OECD.AI Policy Observatory is an inclusive platform that brings together resources and expertise from the OECD and its partners to facilitate dialogue and provide evidence-based policy analysis on the impact of AI. It is built upon the foundation of the OECD AI Principles, the first intergovernmental standard on AI adopted in 2019, endorsed by OECD countries and partner economies.

This article is sponsored by Riskified, and was written, edited and published in alignment with our transparent Emerj sponsored content guidelines.
The advent of the COVID-19 pandemic is leaving retail and eCommerce enterprises awash in numerous, often chaotic trends, many of which are proving critical for driving AI adoption throughout the industry. Perhaps most prominent among these market dynamics is an enormous shift to online retail.

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.

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). 

Insurance is a growing arena for AI adoption and in many cases, automation is leading the way to streamline customer experiences and the organizational pipelines behind them along the entire customer journey.