How exactly does AI model governance help tackle these issues? And how can you ensure you’re using it to best fit your needs? Read on.

For decades, managing data essentially meant collecting, storing, and occasionally accessing it. That has all changed in recent years, as businesses look for the critical information they can pull from the massive amounts of data generated, accessed, and stored in myriad locations, from corporate data centers to the cloud and

AI thrives on data. The more data it can access, and the more accurate and contextual that data is, the better the results will be. The problem is that the data volumes currently being generated by the global digital footprint are so vast that it would take literally millions, if not

As banks and financial institutions seek more granular insights from data with hopes of expanding access to credit, some are leaning on the...

Affine, an AI and data science solutions company plans to hire around 200 people by the end of this financial year. The...

By AI Trends Staff   While AI in hiring is now widely used for writing job descriptions, screening candidates, and automating interviews, it...

Untangling trillions of dollars worth of loans and other financial contracts from Libor is a complex, expensive and time-consuming job.So, finance giants are...

By AI Trends Staff   Advances in the AI behind speech recognition are driving growth in the market, attracting venture capital and...