🔎 What This Study Addresses

Electoral forensics aims to flag anomalies in election results that might signal fraud, but disagreement persists over which tools work best and no standard method exists to combine multiple indicators. Forensic efforts also often ignore country-specific features that could improve diagnosis.

📊 How the Analysis Was Run

• A Bayesian additive regression trees (BART) model, a flexible machine-learning technique, was applied to a large cross-national dataset.
• The model explores a dense network of potential relationships linking: forensic indicators of anomalies, contextual electoral fraud risk factors, and the likelihood of fraud.

🔎 What Was Tested

• Whether multiple forensic tools and country-specific contextual features can be integrated into a single diagnostic framework.
• Which forensic and contextual features carry the most weight in predicting fraud risk when considered together.

📈 Key Findings

• The BART approach can arbitrate among many competing forensic indicators, estimating the relative importance of forensic signals and contextual risk factors.
• Integrating multiple tools and country-specific details yields a practical diagnostic output rather than relying on any single indicator.
• The resulting diagnostic tool is designed for relatively easy implementation in cross-national research settings.

💡 Why It Matters

• Offers a systematic, data-driven way to combine and prioritize forensic indicators across diverse country contexts.
• Provides researchers and analysts with a cross-national diagnostic tool that helps focus follow-up investigation on the most informative signals of electoral irregularities.