Marketing Seminar (2020-02)

Topic:Financial Risk Assessment with Alternative Data: Prediction, Profit, and Equality

Speaker:Yingjie Zhang，The University of Texas at Dallas

Time: Wednesday, 19 August, 9:00-10:30

Microsoft Teams:

https://teams.microsoft.com/l/meetup-join/19%3a1ac5f46cc2e540f5a193cef99579c34b%40thread.tacv2/1596595873365?context=%7b%22Tid%22%3a%2280b7b804-c47e-4119-8274-0f6835b8e89f%22%2c%22Oid%22%3a%22dae73385-f69d-4688-a153-ff18e3659b00%22%7d

Abstract:

The credit risk assessment largely adopted by financial companies suffers from biases due to a lack of control over unobservable factors and the model training based on filtered samples. These days, the high penetration of mobile devices and internet access offers a new source of fine-grained user-behavior data (a.k.a.“alternative data”) for improved financial credit risk assessment. The present study investigates how alternative data from smartphones and social media can be leveraged to offset such biases, and thus simultaneously improve platform revenue and social welfare in loan markets. To this end, we designed a clean, exogenous, and algorithm-independent meta-field experiment that allows for model evaluation under various counterfactual scenarios. Our machine-learning-based empirical analyses reveal the tremendous potential of leveraging alternative data to alleviate financial inequality while achieving higher credit risk prediction accuracy and platform revenue in the meantime. Particularly, cellphone usage and mobility trace information perform the best among the multiple sources of alternative data.

Introduction:

Yingjie Zhang is an assistant professor of Information Systems at the Naveen Jindal School of Management, The University of Texas at Dallas. She received her B.S. degrees in Computer Science and Economics from Tsinghua University and her Ph.D. degree in Information Systems and Management from Heinz College, Carnegie Mellon University. Her research interests center on FinTech, mobile and sensor technologies, big data and smart city, user-generated content, and sharing economy, using interdisciplinary methodologies including structural modeling, machine learning, field experiment, econometrics, and analytical models in IS and marketing. Her research work has been published in the Information Systems Research, Transportation Research Part C, and ACM Transactions on Intelligent Systems and Technology. She is the winner of the Best Paper Award at the International Conference on Information Systems (ICIS 2019) and she is also the winner of the INFORMS ISS Nunamaker-Chen Dissertation Award.

Personal website:https://personal.utdallas.edu/~yingjie.zhang/

Your participation is warmly welcomed!