Survival regression provides a better alternative to a tree model like xgboost when evaluating consumer credit risk and pricing a loan.

1. Using a Survival Regression to
Evaluate
Consumer Credit Risk Instead of
XGBoost
Created by: Salvatore Tirabassi
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2. Traditional Approaches
Classification Algorithms
Utilization of XGBoost and other tree-based classification
algorithms for risk categorization.
Risk-based Loan Pricing
Determining loan pricing strategies based on categorized risk tiers or
dynamic pricing methodologies.
Consumer Credit Profiles
Assessing credit and financial profiles to determine loan pricing and risk
categorization.
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3. Challenges in Credit Risk
Assessment
Prediction Accuracy Real-time Evaluation Dynamic Credit Dynamics
Challenges in achieving
accurate
prediction of payment
defaults and associated
probabilities.
Difficulties in real-
time assessment of
default probabilities
and loan pricing
strategies.
Adapting to the dynamic
nature of consumer
credit while assessing
default
probabilities.
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4. Survival Model Approach
Departure from
Conventional Methods Fixed-income Cash
Flows
Prediction of
Default Probabilities
Shift from traditional
classification
algorithms to the
implementation of
survival regression
models.
Aligning the new
approach with fixed
income
cash flows in the
context of consumer
lending.
Focus on utilizing
survival regression to
predict payment-by-
payment default
probabilities throughout
the loan duration.
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5. Survival Regression in Consumer
Lending
Academic Insights
Research-based evolution from prior academic studies in leveraging survival
regression algorithms for consumer lending.
Hazard & Survival Curves
Utilization of survival regression for calculating hazard function curves and
survival curves for predicting default probabilities.
Payment Default Prediction
Application of survival regression to predict the likelihood of payment-
by-payment default throughout the loan term.
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6. Methodology
Real-time Evaluation
Refinement of the Cox Proportional Hazards algorithm for real-time
evaluation of prospective borrower's financial attributes.
Forward Curve Financing
Incorporating interest rates based on a forward curve for optimizing the Net
Present Value and achieving targeted profitability.
Risk-weighted Cash Flow Series
Assessment of financial attributes to generate risk-weighted cash flow
series for loan pricing and default probability evaluation.
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7. Understanding Hazard and Survival
Curves
Likelihood of Default
Visualization and understanding of the hazard function curve for default
probability at each loan payment.
Probability of No Default
Visualization and understanding of the survival curve for the probability of no
default up to each payment.
Visual Representation
Providing a visual representation of hazard and survival curves for
enhanced comprehension.
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8. Visual Representation
Hazard Function Curve Survival Probability Loan Duration Impact
Visual depiction of the
forecasted default
probability at each loan
payment for applicants.
Visual portrayal of the
likelihood of no
default up to each
payment for loan
applicants.
Analyzing the impact of
loan duration on
the forecasted default
probability and
survival probability.
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9. Benefits of Survival Regression
Accurate Default Assessment Optimized Loan Pricing Tailored Profitability
Improved accuracy in
assessing default
probabilities throughout
the loan term.
Enhanced strategies for
loan pricing based on
real-time risk
assessment and
forward curve financing.
Optimization of Net
Present Value to
achieve targeted
profitability through
riskweighted
cash flow series.
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10. Conclusion
Innovative Approach
Integration of survival regression for nuanced credit risk scoring beyond
conventional XGBoost models..
Real-time Evaluation
Enhanced real-time assessment of default probabilities and loan pricing
strategies.
Transformative Financial Analytics
Promise of accurate default probability assessment and optimized loan
pricing
strategies.
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11. Visit my content
https://salvatoretirabassi.substack.com
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