Detecting Hidden UHNW Customers in Banking The Machine Learning and Big Data Approach
By Mohamed Lemi
The presentation will discuss a banking use case in which advanced machine learning techniques were used to identify hidden UHNW customers by mining massive amounts of data from multiple sources. Potential UHNW customers who were previously unknown to the bank were discovered by tapping into public data sources and combining them with internal customer data. The talk will go over the challenges that were encountered, the solutions that were implemented, and the results that were obtained. Attendees will gain a better grasp of how artificial intelligence and big data can transform the banking industry and deliver significant business value.
About the Speaker
Mohamed is currently the Head of Data Analytics and BI at M7 Group (a CANAL+ Group company) a broadcast media production and distribution organisation. He was also a Manager in Deloitte’s Artificial Intelligence & Data Department, where he leverages his expertise in data science and AI to drive meaningful business results. With deep experience in the banking industry, he has applied a range of data analytics and machine learning techniques to build real-time data products that have delivered significant ROI.
In addition to his professional pursuits, Mohamed is deeply committed to using AI for the betterment of humanity. He has taught machine learning, applied statistics, and coding, and has worked with African governments to develop AI strategies that address key social and economic challenges such as youth unemployment.
Related Projects
about author
Excepteur sint ocaecat cupidas proident sunt culpa quid officia desers mollit sed.
subscribe to newsletter
Receive more Big Data Knowledge article in your inbox: