Mosaic Smart Data Adds Machine-Learning Algo to MSX Trade Analytics Platform

The algo will provide banks' fixed income, currencies, and commodities (FICC) groups with predictive insight into their clients' trading behavior.

matthew-hodgson-ceo-mosaic-smart-data

The vendor's recently launched machine-learning algorithm enables users to predict client activity. For example, based on historical patterns, the algorithm can be used to direct salespeople and traders to clients who have the highest likelihood of interest in a trade, says Mosaic Smart Data founder and chief executive Matt Hodgson.

"The pace of change in the field of data analytics is rapid. As technology vendors continue to work towards providing easy-to-use tools that can be quickly

Only users who have a paid subscription or are part of a corporate subscription are able to print or copy content.

To access these options, along with all other subscription benefits, please contact info@waterstechnology.com or view our subscription options here: http://subscriptions.waterstechnology.com/subscribe

You are currently unable to copy this content. Please contact info@waterstechnology.com to find out more.

Sorry, our subscription options are not loading right now

Please try again later. Get in touch with our customer services team if this issue persists.

New to Waterstechnology? View our subscription options

Register for free

Access two articles, our IMD and Waters Wraps, plus a member newsletter. Find out more.

All fields are mandatory unless otherwise highlighted.

This address will be used to create your account

Banks seemingly build more than buy, but why?

Waters Wrap: A new report states that banks are increasingly enticed by the idea of building systems in-house, versus being locked into a long-term vendor contract. Anthony explores the reason for this shift.

Most read articles loading...

You need to sign in to use this feature. If you don’t have a WatersTechnology account, please register for a trial.

Sign in
You are currently on corporate access.

To use this feature you will need an individual account. If you have one already please sign in.

Sign in.

Alternatively you can request an individual account here