Data-Driven Credit Risk Assessment in U.S. Public Finance: A Review of Rating-Based, Market-Based, and Machine Learning Approaches Under Sparse Default and Delayed Disclosure (International Journal of Innovative Science and Research Technology (IJISRT))

Summary

• The five-year cumulative default rate for Moody's rated municipal universe has remained near 0.08 percent since 2013.p.1
• The primary review window covers academic and institutional literature published between January 2015 and October 2026.p.2
• Municipal financial audits typically require one hundred forty to one hundred eighty days to complete, compared to sixty to ninety days for corporate issuers.p.3
• Over a five-year horizon, municipal credit ratings correctly ranked 97 percent of eventual defaulters below higher-rated peers, compared to 84 percent for global corporate issuers.p.4
• More than 90 percent of municipal spread variation over the 1995 to 2013 period is attributable to liquidity components rather than default risk.p.5
• Machine learning studies applying six algorithms across 49 U.S. states from 2015 to 2017 yielded F1-scores between 53 and 55 percent for an Exactly Balanced Bagging Classifier.p.6

Key insights

1. Assessing ongoing creditworthiness is distinct from predicting the rare binary event of municipal default.p.3
2. Rating agencies incorporate qualitative institutional judgment but remain vulnerable to incentive distortions and reporting delays.p.4
3. Municipal rating stability at the highest level fell to 92.8 percent over the 2013-2022 period compared to the broader 1970-2022 historical average.p.4
4. Market-based signals respond continuously but confound credit risk with tax thresholds, dealer markups, and illiquidity shocks.p.5
5. Machine learning models offer broader early data inclusion but face severe validation obstacles due to the extreme sparsity of default labels.p.6
6. Logistic regression performance in local government distress models demonstrated a wide range of F1-scores from 27 to 58 percent.p.6