Traditional methods of credit scoring and risk assessment are no longer reliable today, because of the large young population coming online, without any previous credit history. Financial firms and NBFCs are trying hard to come up with credible algorithms that can judge if a person is worthy of issuing credit to, without using the traditional methods of credit scoring.
Further, with the boom in FinTech and the startup ecosystem, it is becoming more important to find financial modelling techniques which would work for the newer teams. While it is important to reach the traditionally underbanked sections of the society, technologists must take care to value the privacy, security and agency of the individuals looking for credit.
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.