Course wrap up
Credit Risk Modeling in Python
Michael Crabtree
Data Scientist, Ford Motor Company
Your journey...so far
Prepare credit data for machine learning models
- Important to understand the data
- Improving the data allows for high performing simple models
Develop, score, and understand logistic regressions and gradient boosted trees
Analyze the performance of models by changing the data
- Understand the financial impact of results
- Implement the model with an understanding of strategy
Risk modeling techniques
The models and framework in this course:
- Discrete-time hazard model (point in time): the probability of default is a point-in-time event
- Stuctural model framework: the model explains the default even based on other factors
Other techniques
- Through-the-cycle model (continuous time): macro-economic conditions and other effects are used, but the risk is seen as an independent event
- Reduced-form model framework: a statistical approach estimating probability of default as an independent Poisson-based event
Choosing models
Many machine learning models available, but logistic regression and tree models were used
- These models are simple and explainable
- Their performance on probabilities is acceptable
Many financial sectors prefer model interpretability
- Complex or "black-box" models are a risk because the business cannot explain their decisions fully
- Deep neural networks are often too complex
Tips from me to you
Focus on the data
- Gather as much data as possible
- Use many different techniques to prepare and enhance the data
- Learn about the business
- Increase value through data
Model complexity can be a two-edged sword
- Really complex models may perform well, but are seen as a "black-box"
- In many cases, business users will not accept a model they cannot understand
- Complex models can be very large and difficult to put into production
Thank you!
Credit Risk Modeling in Python
