Credit risk is more than predicting default: building the full stack in Python (IFRS 9 ECL, scorecards, monitoring)
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Most credit-risk tutorials stop at "train a classifier to predict default." That is maybe a fifth of what a real credit-risk function does, and not the interesting fifth. So I built the rest, on 1.35 million real loans, as three connected projects: an IFRS 9 expected credit loss engine (PD, LGD, EAD, staging, macro scenarios), a Weight-of-Evidence scorecard plus an independent model validation , and a portfolio monitoring and management-information pack . Stack is deliberately boring and…
1Key Takeaways
- Most credit-risk tutorials stop at "train a classifier to predict default." That is maybe a fifth of what a real credit-risk function does, and not the interesting fifth.
- So I built the rest, on 1.35 million real loans, as three connected projects: an IFRS 9 expected credit loss engine (PD, LGD, EAD, staging, macro scenarios), a Weight-of-Evidence scorecard plus an independent model validation , and a portfolio monitoring and management-information pack .
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that most credit-risk tutorials stop at "train a classifier to predict default." That is maybe a fifth of what a real credit-risk function does, and not the interesting fifth.
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