Predictive analytics plays an important role in the banking industry when it comes to fraud prevention, risk assessment, and more. Discover how explainable AI can enhance the functions predictive analytics serves in banking.
Facebook recently announced that it will no longer support 3rd Party Data Partner Categories or enable advertisers to create campaigns against custom audiences in their platform due to privacy regulations.
According to CIO Magazine, May 2nd, 2018, major brands are intensely searching for explainable AI solutions for several reasons spanning guarding against ethical and regulatory breaches to understanding when to override machine based decisions or how to improve them in the future.
In Cognilytica Research’s briefing note on simMachines, January, 2018, it highlights the black box challenge of today’s AI machine learning technologies and the fundamental problems lack of explainability causes.
simMachines, Inc., the leader in Explainable AI / Machine Learning applications, announces the launch of their latest product—Dynamic Predictive Audiences designed for data companies, publishers and media platforms.
Today’s digitally-empowered customers have high expectations for relevant, highly personalized customer experiences. Companies must keep pace with these growing demands or they will be left behind by their more perceptive competition.
Similarity is a machine learning method that uses a nearest neighbor approach to identify the similarity of two or more objects to each other based on algorithmic distance functions.
Market segmentation is one of the most basic arms of business strategy. Firms bundle customers to understand their preferences, manage relationships with them, improve product and service offerings, and assess risk.
It’s well known that the “Amazon effect” is wreaking havoc on retailers’ ability to lure and retain shoppers because of their inability to predict what motivates customers to buy and to be relevant in the moment of interaction.