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Course Outline
Introduction to Multimodal AI for Finance
- Overview of multimodal AI and its financial applications
- Types of financial data: structured vs. unstructured
- Challenges in financial AI adoption
Risk Analysis with Multimodal AI
- Fundamentals of financial risk management
- Using AI for predictive risk assessment
- Case study: AI-driven credit scoring models
Fraud Detection Using AI
- Common types of financial fraud
- AI techniques for anomaly detection
- Real-time fraud detection strategies
Natural Language Processing (NLP) for Financial Text Analysis
- Extracting insights from financial reports and news
- Sentiment analysis for market prediction
- Using LLMs for regulatory compliance and auditing
Computer Vision in Finance
- Detecting fraudulent documents with AI
- Analyzing handwriting and signatures for authentication
- Case study: AI-driven check verification
Behavioral Analysis for Fraud Detection
- Tracking customer behavior with AI
- Biometric authentication and fraud prevention
- Analyzing transaction patterns for suspicious activities
Developing and Deploying AI Models for Finance
- Data preprocessing and feature engineering
- Training AI models for financial applications
- Deploying AI-based fraud detection systems
Regulatory and Ethical Considerations
- AI governance and compliance in financial institutions
- Bias and fairness in financial AI models
- Best practices for responsible AI use in finance
Future Trends in AI-Driven Finance
- Advancements in AI for financial forecasting
- Emerging AI techniques for fraud prevention
- The role of AI in the future of banking and investments
Summary and Next Steps
Requirements
- Basic knowledge of AI and machine learning concepts
- Understanding of financial data and risk management
- Experience with Python programming and data analysis
Audience
- Finance professionals
- Data analysts
- Risk managers
- AI engineers in the financial sector
14 Hours
