ID:A Labs is our research group that is dedicated to expanding our understanding of identity risk, consumer behavior, and the potential of new technology.

The analytical modeling team behind ID:A Labs is made up of a multidisciplinary group of mathematicians, computer scientists, economists, financial experts, cognitive scientists, and advisors from ID Analytics. They are passionate about exploring new methods to solve the complex problems that impact our customers.
 

How We Work

ID:A Labs is focused on furthering our technology strategy and innovation through ongoing research and analysis on developments in consumer behavior, identity and credit-related issues, and innovations in analytics around modeling and machine learning.

Meet Our Scientists

Sunhyoung Han is a senior principal scientist with ID Analytics. She applies her passion for data science to finding the truth and order behind the messiness of data. Sun enjoys analyzing data to understand how patterns of consumer behavior impact our clients and the solutions we develop for them. Sometimes her discoveries may lead to a new predictive model, or they may help a client optimize their policies to improve results.

Sun describes working at ID Analytics as different from other places she has worked because our data scientists have the freedom to make their own contributions to innovation and new product development. She believes that this approach stimulates people to think about problems in various new ways while sharing a common vision and direction. Data scientists at ID Analytics are encouraged to learn about all of the different business problems that we solve for our clients and to work fluidly across projects to offer varied perspectives.

Our Latest Research

  • ID3D – a new way of visualizing the entire space of risk to help evaluate user intent.
  • Deep Neural Nets – Inspired by visions systems, deep learning allows unsupervised formation of internal structures in data.
  • Heirarchical Clustering – the ability to smoothly and accurately move across hierarchical entity levels.