Modeling and Analytics Strategy

Balanced Approach

Balanced approach:
- Balance the complexities and the simplicity
  - Simplicity: user experience, operational efficiency
- Balance the data and the experts
- Overrides are not necessarily an indicator of the failure of the modeling and analytics. They are an indicator of the complexities of the subject.
- Critical decision points: provide 2–3 alternatives and make a recommendation
- Meet the business requirements and regulatory requirements

Research

Understand the complexities

Design

Make them simple

Quantitative

Primary support: Data
Secondary support: Experts

Qualitative

Primary support: Experts
Secondary support: Data

Override

Account for the complexities

Modeling and Analytics Partnership

Communication and Documentation

Feedback loop: constant communication
Weekly working group meetings: modelers only
Email correspondence: also serve as evidence
Invite LOB, credit, and model risk to the meeting only when necessary to get consensus
Provide 2–3 alternatives at every critical decision point

Modelers

Business &
Credit Users

Model Validators

Use the model documentationtemplate as a guideline

Meet the operational requirements

PD Models

Target Variable Approaches

Target Variable	Description	Pros	Cons
Binary event (default, non-default)	Modeling the "ground truth" – the objective, observable event of default Model predicts PD → PD is mapped to an ORR based on the master scale	Conceptual soundness & objectivity: the model is based on actual default events rather than subjective rating assignments. This creates a direct, unbroken chain of logic from borrower characteristics to the default risk. Binary PD models are standard in regulatory frameworks (e.g., Basel, etc.) Simpler modeling approach	Significant information loss in the target variable Calibration challenges: PD model must be well‑calibrated to avoid distorted ratings Significant challenges for the low‑default portfolios
Obligor Risk Ratings	Modeling the "human process" – replicating the expert judgment that goes into assigning a rating Model predicts ORR → ORR is mapped to a PD based on the master scale	Richer information in the target variable Captures embedded expert judgement: the model can learn the experts' collective wisdom and align with internal practices Historical consistency: maintains better continuity with existing rating methodologies Works better for the low‑default portfolios	Subjectivity and circular: the model's validity is entirely dependent on the historical quality and consistency of the very rating process it is trying to replicate May have higher regulatory scrutiny due to a subjective foundation Increased model complexity

Target Variable

Description

Pros

Cons

Binary event
(default, non-default)

Modeling the "ground truth" – the objective, observable event of default

Model predicts PD → PD is mapped to an ORR based on the master scale

Conceptual soundness & objectivity: the model is based on actual default events rather than subjective rating assignments. This creates a direct, unbroken chain of logic from borrower characteristics to the default risk.
Binary PD models are standard in regulatory frameworks (e.g., Basel, etc.)
Simpler modeling approach

Significant information loss in the target variable
Calibration challenges: PD model must be well‑calibrated to avoid distorted ratings
Significant challenges for the low‑default portfolios

Obligor Risk Ratings

Modeling the "human process" – replicating the expert judgment that goes into assigning a rating

Model predicts ORR → ORR is mapped to a PD based on the master scale

Richer information in the target variable
Captures embedded expert judgement: the model can learn the experts' collective wisdom and align with internal practices
Historical consistency: maintains better continuity with existing rating methodologies
Works better for the low‑default portfolios

Subjectivity and circular: the model's validity is entirely dependent on the historical quality and consistency of the very rating process it is trying to replicate
May have higher regulatory scrutiny due to a subjective foundation
Increased model complexity

Modeling Approaches

Category	Model	Pros	Cons
Traditional Regression Models	Logistic Regression Probit Regression	Transparent: coefficients provide clear insights into the risk drivers Well‑understood statistical properties; easy to validate and explain to stakeholders Widely accepted by regulators	Assumes linear relationships between variables; limited ability to capture complex interactions Requires a sufficient number of default observations, which can be challenging for low‑default portfolios May underperform compared to machine learning models
Traditional Regression Models	Cox Proportional Hazards Model	Naturally handles time‑to‑default dynamics Incorporates censored observations effectively Handles varying observation periods effectively	Less common in PD modeling The proportional hazards assumption may not hold Requires sufficient default events for stable estimation
Machine Learning Models	Random Forest	Captures non‑linearity & interactions effectively Handles outliers & missing data well Less prone to overfitting: averaging across trees reduces variance	Black‑box nature can challenge regulatory approval and internal validation Less stable: model results may vary across different runs due to bootstrapping
	Gradient Boosting	High predictive power – often outperforms traditional models and random forests Captures non‑linearity & interactions effectively Handles outliers & missing data well	Even harder to explain than random forests due to additive tree structure High risk of overfitting, requiring careful tuning Requires large datasets for optimal performance
	Neural Network	High performance: with enough data, it can outperform other models Highly flexible: learns complex interactions automatically Feature learning: deep learning can extract latent features	Extremely hard to explain to regulators and stakeholders Requires large amounts of data to train effectively and avoid overfitting Highly complex: requires significant expertise to design the network architecture (layers, nodes, activation functions) and tune the training process