Product · 8 documents
The study develops a scalable MLOps framework evaluated on 100,000 transactions sampled from the Credit Card Transactions Fraud Detection Dataset.
The proposed hybrid AI system was trained and evaluated on a transaction dataset containing more than 6 million records with transaction time steps ranging from 0 to 743.
Machine learning algorithms outperform traditional regression techniques by a margin of generally 10%–15% in loan default prediction.
The framework addresses extreme class imbalance in a dataset comprising 6,362,620 transaction records, where only 8,213 records (0.1291%) represent fraudulent activity.
On a six-million-transaction synthetic dataset, the proposed ensemble attains a recall of 0.9184 at a false-positive rate of 0.0186.
This study proposes the Bitcoin Influence Index (BII) to weight social media sentiment using four influence dimensions: Reach, Credibility, Activity, and Content Influence.
RiskPercept-X achieves 89.0% accuracy and 86.8% macro-averaged F1 on risk dimensions on a temporally held-out split.
AI governance integrates explainability, model risk management, lifecycle governance, and internal controls to satisfy regulatory expectations in banking.
