Using CI and AI to Tackle Top Financial Services Regulatory Challenges
The complexity and ever-changing nature of fraud and financial crime patterns requires an ability to quickly and effectively spot and stop
Looks at issues related to artificial intelligence technologies, including cognitive computing, deep learning, and machine learning. Considers also supervised and unsupervised learning and natural language processing.
The complexity and ever-changing nature of fraud and financial crime patterns requires an ability to quickly and effectively spot and stop
To bridge the gap between the data we're collecting and the way organizations interface with it, we need to address some uncomfortable
Many businesses believe they need AI, but one of the biggest challenges is finding
In the news this week: AI and ML solutions to future-proof vehicles, an update to an open-source multi-model database, a cognitive advertising accelerator, and …
The new tool uses a machine learning neural net and is said to be up to 50 percent more accurate than hand-crafted models.
As AI brings ethical and national security issues to the table, one new rule is hoping to keep sensitive software out of the hands of
Constraint solvers take a set of hard and soft constraints in an organization and formulate the most effective plan, taking into account real-time
Corporate AI offerings must be as robust and feature rich as the consumer AI services workers use at home. Otherwise, users will not embrace
Embedded development is often driven by the need to deploy highly optimized and efficient
Business leaders need to invest more in creating a future-ready workforce to overcome skills gaps that can impede progress towards a real-time