Integrating Business Insights into Data Science

Big Data and Analytics Roundtable

July 25, 2019: 6:30 PM - 8:45 PM

Shift from quantitative models to incorporating qualitative insights for deriving better business insights

Where

Gleacher Center
450 Cityfront Plaza Dr
Chicago, Illinois

Event Details

Data science has grown into every aspect of the business world. More and more business traditionally relying on business judgement and experience to make decisions have now utilized data to inform decision-making. This trend has created new challenges for data scientists: not only do the data scientists have to communicate with their stakeholders more effectively but are also tasked with incorporating qualitative insights into quantitative models more directly.

Mr.Wang will discuss three use cases in risk analytics that cover causal inference, machine learning, and Value-at-Risk. These use cases will explore how to utilize the insights from subject matter experts to enhance quantitative models:

  1. Causal Inference: discuss how historical knowledge about the business can be used to construct a quasi-experimental design to estimate the causal effect when randomized control trials are not possible.
  2. Machine Learning: discuss how to incorporate business insights into the feature engineering step to increase signal-to-noise ratio and hence prediction accuracy.
  3. Value-at-Risk: discuss how to use a Bayesian framework to combine historical data (likelihood) and subject matter experts' qualitative assessments (prior) to infer capital allocation.

Cost

  • Early $10
  • Week of $15
  • At the Door $20

Registration

Register Online

Deadline: 7/25/2019

Speaker Profiles

Ming-Sen Wang (Speaker)
Vice President in Operational Risk Analytics, Northern Trust
https://www.northerntrust.com

Ming-Sen Wang is currently Senior Consultant – Vice President in Operational Risk Analytics at Northern Trust. In his current role, he develops models for loss forecasting, risk quantification, and capital allocation to support enterprise capital planning. He also serves as an internal consultant for analytics projects including fraud prevention, cloud computing, machine learning, and SOFR-LIBOR transition. Ming-Sen is actively promoting the use of advanced analytics in operational risk. In particular, he is an active participant in the machine learning working group and the industry-wide text mining project with Operational Riskdata eXchange Association (ORX). Ming-Sen received his Ph.D. in economics from the University of Arizona where he specialized in applied econometrics and program evaluation

Questions

Roger Moore, '92 
VP, Analytics / Entytle, Inc