Date: 22 January 2019 Time: 5:00 PM to 6:30 PM
Traditional methods of credit scoring and risk assessment have limitations when dealing with populations who don’t have previous credit history. Digital trails created by online activities such as social media, telecom data can be used as inputs for alternative credit scoring models. Financial firms and NBFCs are working hard to come up with credible algorithms, using these alternative models.
How do these models work in the real world and do they actually help in financial inclusion of previously underbanked sections of the society? What should technologists who are working in these areas do to address privacy, security and agency of the individuals?
If you’re working on financial inclusion, modelling, or working with alternate ways to assess credit worthiness, this session is for you! The Fireside chat would be between 5:00 - 6:30 PM on 22 January. The panelists will begin with a short introduction, followed by context-setting of the topic.
Join Thuong Nguyen, Chris Stucchio and Anand V as they chat on the various techniques in financial modelling, credit scoring and risk assessment.
- Introduction to credit scoring models.
- VAR (Value at Risk) models and their relevance to Credit scoring.
- Are credit scores calculated by non-standard models, the only reasonable alternative for financial inclusion?
- How digital trails can be used to create alternate credit scores? Are social media profiles a better indicator than collaterals?
- Case Study - Instant lending services, insurance etc.
- Data sources used: Privacy & Security aspects.
- Policy and regulations around use of alternative data.
About the speakers:
Thuong is a research scientist working on advanced machine learning and big data to solve financial problems 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 work have been published in several leading conferences and journals.
Chris is the head of Data Science at Simpl. Simpl’s mission is to make money simple, so that people can live well and do amazing things. He is also a former physicist, high frequency trader and software developer. He’s been working in decision theory and bayesian optimization for the past 5 years, and has been teaching statistics to novices for much longer.
Anand V is a security researcher who also dabbles in financial modelling and large scale infrastructure.
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