Five AI Solutions Every Retail Bank Needs
Retail banks are among the most sophisticated users of machine learning among all other industries. In recent years, they have developed automated loan approval processes, credit scoring systems, targeted marketing capabilities, and a variety of other types of solutions. And they have done this as a matter of necessity. The number of upstart fintech companies in the lending and payments spaces has grown exponentially, and they are gobbling up market share. Retail banks must dramatically expand their use of AI and machine learning to optimize every part of their business or risk failure. This 20-minute overview highlights five key AI solutions that all retail banks need to develop to be competitive. These solutions will dramatically boost revenue and improve competitive position, while at the same time uncover new ideas and opportunities.
DJ Human is a data scientist within the banking vertical of DataRobot in EMEA based out of South Africa. Apart from providing data science expertise on the most challenging problems DataRobot’s banking customers face, DJ also leads MLOps engagements across Southern and Northern Europe, Africa and the Middle East. Prior to joining DataRobot, he was a quantitative analyst within FNB, one of the largest banks in Africa. He holds MCom in Operations Research from Stellenbosch University and a BSc in Mathematical Sciences, also from Stellenbosch University.