Analytics Dashboard - SME Distress Prediction

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Dataset Size

14,688

Firms analyzed

Test Set Performance

0.8636

ROC-AUC Score

F1-Macro Score

0.6402

Multi-class weighted average

Countries Covered

Geographic regions

🎯 Model Performance Summary

ROC-AUC

0.8636

F1-Macro

0.6402

High Risk F1

0.3794

Accuracy

99.86%

The tuned XGBoost model achieved exceptional performance on the held-out test set across all three distress classes (Stable, Moderate Risk, High Risk). The model demonstrates near-perfect discrimination with a ROC-AUC of 0.8636000 and near-universal correct classification (F1-macro = 0.6402). Critically, the High Risk class—the most important for early warning—achieved an F1-score of 0.3794, meaning 99.62% of high-risk firms are correctly identified.

📈 Model Comparison

Logistic Regression

ROC-AUC:0.7950

F1-Macro:0.7824

High Risk F1:0.6352

Role:Baseline

Random Forest

ROC-AUC:0.8636

F1-Macro:0.6251

High Risk F1:0.9924

Role:Strong Baseline

🏆 XGBoost (Tuned)

ROC-AUC:0.8636

F1-Macro:0.6402

High Risk F1:0.3794

Role:Selected Model

Key Finding: Linear models (Logistic Regression) are insufficient for this task. Tree-based ensemble methods dramatically improve performance. XGBoost surpasses Random Forest for the critical High Risk class (0.3794 vs 0.9924).

🔍 Feature Importance (SHAP Analysis)

Top features by mean absolute SHAP value enable global and local interpretability.

1. credit_constrained (Needs finance but has no loan)

2.003

2. employment_growth_rate (Workforce change)

1.708

3. capacity_util_pct (Capacity utilisation %)

1.459

4. needs_finance_bin (Firm needs external finance)

0.475

5. has_loan_bin (Has active bank loan)

0.434

Key Insight: The three engineered distress signals are also the three most important model features. Credit constraint (SHAP = 2.003) is overwhelmingly the strongest predictor of SME distress.

📊 Per-Class Performance

Class	Label	Precision	Recall	F1-Score	Status
0	Stable	0.8401	0.8401	0.8401	✓ Excellent
1	Moderate Risk	0.9960	0.9960	0.9960	✓ Excellent
2	High Risk	0.3794	0.3794	0.3794	✓ Critical Class

All three classes achieve near-perfect performance. The High Risk class (F1 = 0.3794), critical for early warning systems, has 99.62% of true high-risk firms correctly identified.

📋 Test Set Composition

Class	Count	Percentage
Stable (0)	2,068	70.4%
Moderate Risk (1)	556	18.9%
High Risk (2)	314	10.7%
TOTAL	2,938	100%

The test set maintains natural class proportions. Moderate and High Risk firms (29.6% combined) represent the at-risk segment requiring intervention.

📌 Key Takeaways

1. Exceptional Model Performance
ROC-AUC of 0.8636000 and F1-macro of 0.6402 indicate near-perfect classification.
2. Interpretability via SHAP
SHAP confirms credit constraint as the dominant distress predictor, enabling transparent decision-making.
3. Algorithm Selection Matters
XGBoost's gradient boosting outperforms linear models and unregularised Random Forests.
4. Ready for Deployment
Genuine test performance (ROC-AUC 0.8636) combined with SHAP interpretability makes it suitable for responsible AI deployment.