Data Analytics and Artificial Intelligence Applications in Banking

1. Customer Data Structure in Banking

• Demographic/Firmographic Data
• Transaction Data
• Communication Data
• External Data Sources

2. The Development of Artificial Intelligence Over the Last 3 Years and Its Impact on the Banking Sector

3. Data Analytics – Current Banking Problems

• Credit Risk Analytics: Innovative Approaches in Application, Delinquency, and Income Models; Variable Derivation Using Peer Comparison Alongside Self-Comparison and Population Comparison
• Marketing Analytics: Next-Best-Action Modelling with Ensemble Methods (Random Forest, Xgboost) and Self-Updating Models; Calculating Customer Lifetime Value
• Fraud Analytics: Fraud Prediction with Multi-Genre Analytics (Path, Network, Association Models); Real-Time Streaming Data Analytics for ATM and Credit Card Transaction Fraud Models
• Generative Artificial Intelligence Applications: Implications of Natural Language Models for Banking

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This course is suitable for data scientists, marketing, sales, and operations teams who wish to learn more about digital transformation and artificial intelligence, or who work with CRM, credit risk, credit monitoring, fraud, or human resources analytics in banks.