Natalino Busa, Head of Applied Data Science at Teradata
The best services have one thing in common: a superb customer experience. Banking services are no exception to this rule, and indeed the quest for an effortless, well informed, and personalized customer experience is one of the main goals of today's innovation in digital banking services.
According to what Maslow has described in his "pyramid of needs", customers are seeking a more intimate and meaningful experience where banking services can actively assist the customer in performing and managing their financial life. Predictive APIs have a fundamental role in all this, as they enable a new set of customer journeys such as automatic categorization of transactions, detecting and alerting recurrent payments, pre-approving credit requests or provide better tools to fight fraud without limiting legitimate customer transactions.
In this talk, I will focus on how to provide better banking services by using predictive APIs. I will describe the path on how to get there and the challenges of implementing predictive APIs in a strictly audited and regulated domain such as banking. Finally, I will briefly introduce a number of data science techniques to implement those customer journeys and describe how big/fast data engineering can be used to realize predictive data pipelines.
The presentation will unfold in three parts:
1) Define banking services: Maslow's law, modern vs traditional banking
2) Examples predictive and personalized banking experiences
3) Examples of data science and data engineering pipelines for banking and financial services