Get 35% Off CFI Certifications - Code CFI35
AI Engineer - Learn how to integrate AI into software applications
Overview
Coursera Flash Sale
40% Off Coursera Plus for 3 Months!
Grab it
Explore deep learning survival analysis techniques for consumer credit risk modeling in this 31-minute conference talk by Jiahang Zhong, PhD at ODSC Europe 2019. Gain insights into the importance of accurate credit risk prediction at the individual level for consumer lending and credit card businesses. Learn about the evolution from traditional probabilistic outcome predictions to more precise time-to-event estimates using survival analysis. Discover how recent advancements in big data and deep learning have enabled sophisticated survival models for individual-level predictions. Review theoretical concepts of survival analysis and classic models before delving into the integration of deep learning models in the context of consumer lending. Examine topics such as credit risk scorecards, types of supervised learning, classic survival models, survival in the machine learning era, deep learning survival models, censorship assumptions, and competing hazard objective functions. Enhance your understanding of cutting-edge approaches to credit risk modeling and their potential impact on providing better products to customers and establishing competitive advantages in the market.
Syllabus
Intro
Credit Risk of Personal Loans
Credit Risk Scorecard
Types of supervised learning
Survival analysis
Classic Survival Models
Survival in ML era
Deep Learning Survival Models
Predictions
Censorship assumption
Competing hazard objective function
Competing hazard model
Taught by
Open Data Science
Reviews
4.0 rating, based on 1 Class Central review
Showing Class Central Sort
-
Great Through the course, I gained insights into how neural networks and time-to-event modelling enhance the assessment of borrower risk beyond static credit scoring approaches. The lessons emphasized the use of hazard functions, Kaplan-Meier estimations, and Cox proportional hazards models—adapted into deep learning frameworks—to estimate credit default timelines.
A particularly valuable component was learning to handle imbalanced data and censored information, common in consumer lending datasets. The hands-on exercises with Python and TensorFlow provided practical exposure to building survival models for predicting loan default and prepayment risks.