In the context of banking, which of the following is a primary use case of Data Science?

Credit scoring models (logistic regression, random forests, gradient boosting, neural networks) analyse features like repayment history, credit utilization, income stability, loan-to-income ratio, and bureau scores (CIBIL) to predict default probability.  Other major banking Data Science use cases:  • Fraud detection (anomaly detection, real-time transaction scoring) • Customer churn prediction • AML (Anti-Money Laundering) transaction monitoring • Cross-sell/upsell recommendations • Sentiment analysis of customer feedback • Regulatory capital modelling