Advancing Data Science for Financial Inclusion: Trusting Social's Journey
Submitted by Thuong Nguyen (@thuong) on Tuesday, 15 January 2019
Technical level: Intermediate Status: Confirmed & Scheduled
Traditional approach to Credit Scoring requires financial histories of customers. As a result, it keeps unbanked customers, especially in developing economies, out of the loop. To solve this problem, Trusting Social utilizes alternative sources of data to assess creditworthiness of the whole population to deliver financial access for all.
In this talk, we will discuss about the motivation for alternative credit scoring, Data Science challenges that we faced during our journey as well as the insights.
- Introduction to credit scoring
- Limitation of credit scoring
- Alternative credit scoring
- Data Science challenges in alternative credit scoring
- How Trusting Social works?
Thuong is a research scientist working on advance machine learning and big data to solve financial problem such as credit scoring. His machine learning and data science experience spreads across multiple fields including mobile and social networks, pervasive computing, Internet of Things, e-health, and finance, in both academia and industrial environments. His research works have been published in several leading conferences and journals.