There is a lot of hype about machine learning in financial services, but few concrete examples of its application. One potentially important application is to gain a better understanding of customers and their needs – a task that lies at the heart of any business.
In this paper, we use a case study to reveal how raw data can be harnessed to build insights into distinct customer groups, through a combination of data science, machine learning and design thinking. Our innovative Scientific Personas methodology interweaves qualitative and quantitative approaches, to explore and validate new insights.
This interweaving allows us to build a process that is scalable, yet also fine-tuned, and personalized to the lowest granularity. Download the PDF to find out more.
For more information about how data science adds significant value in financial services across multiple functions, and how we are helping firms today, please contact:
Riddhi Sen, Head of Data Science, Machine Learning and Intelligent Automation